STATA – Pola Konsumsi LA-AIDS (Syntax)

Syntax data LA/AIDS dengan STATA
March 6, 2018
Tableau – World Current Account
May 15, 2018
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Microdata Analysis Pola Konsumsi Pangan dengan Metode Linear Approximation Almost   Ideal Demand System (LA/AIDS)

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. **STEP 1. DATASET VARIABEL INDEPENDEN SUSENAS KOR INDIVIDU

. *1.1. Usia Kepala Rumah Tangga (age)

. *1.2. Jenis Kelamin Kepala Rumah Tangga (jk)

. *1.3. Pendidikan Kepala Rumah Tangga (edu)

. *1.4. Pekerjaan Kepala Rumah Tangga (work)

.

. *1.1. Dataset Usia KRT (age)

. * Membuka data Susenas Kor individu

. use $datakor16ind\kor16ind_35.dta, clear

 

. * Merubah urutan variabel yang ada dalam file data sesuai urutan

. order urut r403 r407

 

. * Menyimpan variabel-variabel yang akan digunakan

. keep urut r403 r407

 

. * Menyimpan variabel yang akan digunakan dengan menggunakan syarat tertentu

. keep if r403==1

(72389 observations deleted)

 

. * Menjumlahkan data, note : setiap selesai running dataset hendaknya dilakukan koreksi terhadap jumlah data agar jumlah konsisten sampai dengan akhir.

. count

29467

 

. * Membuang variabel yang tidak digunakan

. drop r403

 

. * Mengganti nama variabel lama dengan nama baru

. rename r407 age

 

. * Memberi nama variabel

. label variable age “umur KRT”

 

. * Mengurutkan data berdasarkan variabel tertentu

. sort urut

 

. * Menyimpan file sementara

. tempfile age

 

. * Menyimpan file

. save `age’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000001.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000001.tmp saved

 

 

. *1.2. Dataset Jenis Kelamin KRT (jk)

. ***   Dummy variabel (0=perempuan dan 1=laki-laki)

. use $datakor16ind\kor16ind_35.dta, clear

 

. order urut r403 r405

 

. keep urut r403 r405

 

. keep if r403==1

(72389 observations deleted)

 

. count

29467

 

. drop r403

 

. rename r405 jk

 

. replace jk=0 if jk==2

(5424 real changes made)

 

. label variable jk “jenis kelamin KRT”

 

. sort urut

 

. tempfile jk

. save `jk’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000002.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000002.tmp saved

 

.

. *1.3. Dataset Pendidikan KRT (edu)

1

. ***   Dummy variabel (0=SLTA kebawah dan 1=SLTA keatas)

. use $datakor16ind\kor16ind_35.dta, clear

 

. order urut r403 r510

 

. keep urut r403 r510

 

. keep if r403==1

(72389 observations deleted)

 

. * Menciptakan variabel baru

. gen edu=1 if r510>=12

(19351 missing values generated)

 

. * Menggantikan (overwrite) variabel yang sudah ada

. replace edu=0 if (r510<12 | r510==.)

(22485 real changes made)

 

. label variable edu “pendidikan KRT”

 

. count

29467

 

. drop r403 r510

 

. sort urut

 

. tempfile edu

 

. save `edu’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000003.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000003.tmp saved

 

.

. *1.4. Dataset Pekerjaan KRT (work)

. ***   Dummy variabel (1=tidak bekerja, 2=sektor non pertanian, 3=sektor pertanian)

. use $datakor16ind\kor16ind_35.dta, clear

 

. order urut r403 r1103

 

. keep urut r403 r1103

 

. keep if r403==1

(72389 observations deleted)

 

. gen work=3 if r1103==1

(19259 missing values generated)

 

. replace work=2 if (r1103>=2 | r1103==0)

(19259 real changes made)

 

. replace work=1 if r1103==.

(4311 real changes made)

 

. * Membuat tabulasi suatu variabel (perhitungan seberapa banyak jumlah dari suatu variabel)

. tab work, gen (work_i)

 

work |      Freq.     Percent        Cum.

————+———————————–

1 |      4,311       14.63       14.63

2 |     14,948       50.73       65.36

3 |     10,208       34.64      100.00

————+———————————–

Total |     29,467      100.00

 

. label define work 1″1.tidak bekerja” 2″2.sektor non pertanian” 3″3.sektor pertanian”

 

. label val work work

 

. label var work “pekerjaan KRT”

 

. label variable work_i1 “tidak bekerja”

 

. label variable work_i2 “sektor non pertanian”

 

. label variable work_i3 “sektor pertanian”

 

. count

29467

 

. drop r403

 

. sort urut

 

. tempfile work

 

. save `work’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000004.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000004.tmp saved

 

.

. **STEP 2. DATASET VARIABEL INDEPENDEN SUSENAS KOR RUMAH TANGGA

. *2.1. Tipologi Wilayah (wil)

. *2.2. Status Kepemilikan Rumah (Milik)

. *2.3. Status Kemiskinan Rumah Tangga (Miskin)

. *2.4. Status IPM daerah rumah tangga berada (ipm)

.

. *2.1. Dataset Tipologi Wilayah (wil)

. ***   Dummy variabel (0=perdesaan dan 1=perkotaan)

. use $datakor16rt\kor16rt_35.dta, clear

 

. order urut r105

 

. keep urut r105

 

. replace r105=0 if r105==2

(14053 real changes made)

 

. count

29467

 

. rename r105 wil

 

. label variable wil “tipologi wilayah”

 

. sort urut

 

. tempfile wil

 

. save `wil’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000005.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000005.tmp saved

 

.

. *2.2. Dataset Status Kepemilikan Rumah (milik)

. ***   Dummy variabel (0=bukan milik sendiri dan 1=milik sendiri)

. use $datakor16rt\kor16rt_35.dta, clear

 

. order urut r1502

 

. keep urut r1502

 

. replace r1502=0 if r1502>=2

(2609 real changes made)

 

. count

29467

 

. rename r1502 milik

 

. label variable milik “status kepemilikan rumah”

 

. sort urut

 

. tempfile milik

 

. save `milik’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000006.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000006.tmp saved

. *2.3. Dataset Status Miskin Rumahtangga (1=miskin dan 0=bukan miskin)

. use $datakor16rt\kor16rt_35.dta, clear

 

. order urut r105 exp_cap

 

. keep urut r105 exp_cap

 

. gen miskin=1 if (r105==1 & exp_cap<319662)

(28400 missing values generated)

 

. replace miskin=1 if (r105==2 & exp_cap<323779)

(1994 real changes made)

 

. replace miskin=0 if (r105==1 & exp_cap>=319662)

(14347 real changes made)

 

. replace miskin=0 if (r105==2 & exp_cap>=323779)

(12059 real changes made)

 

. count

29467

 

. label variable miskin “status miskin rumah tangga”

 

. sort urut

 

. tempfile miskin

 

. save `miskin’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000007.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000007.tmp saved

 

.

. *2.4. Dataset Status IPM daerah rumah tangga berada (ipm)

. ***   Dummy variabel (0=rendah/sedang dan 1=tinggi/sangat tinggi)

. use $datakor16rt\kor16rt_35.dta, clear

 

. order urut r102

 

. keep urut r102

 

. gen ipm=1 if r102==4|r102==15|r102==16|r102==17|r102==18|r102==20|r102==24

(23728 missing values generated)

 

. replace ipm=1 if r102==25|r102==71|r102==72|r102==73|r102==74|r102==75|r102==76

(4128 real changes made)

 

. replace ipm=1 if r102==77|r102==78|r102==79

(2048 real changes made)

 

. replace ipm=0 if r102==1|r102==2|r102==3|r102==5|r102==6|r102==7|r102==8|r102==9

(6917 real changes made)

 

. replace ipm=0 if r102==10|r102==11|r102==12|r102==13|r102==14|r102==19|r102==21

(5830 real changes made)

 

. replace ipm=0 if r102==22|r102==23|r102==26|r102==27|r102==28|r102==29

(4805 real changes made)

 

. count

29467

 

. drop r102

 

. label variable ipm “status ipm daerah rumah tangga berada”

 

. sort urut

 

. tempfile ipm

 

. save `ipm’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000008.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000008.tmp saved

 

 

 

 

. **STEP 3. DATASET VARIABEL DEPENDEN DARI SUSENAS MODUL KONSUMSI PANGAN

. *3.1. Dataset Kelompok Makanan

. *3.2. Pengelompokkan komoditi pangan

. *3.3. Menghitung Total Pengeluaran Setiap Kelompok Komoditi Pangan

. *3.4. Membuat Variabel Dependen

.

 

 

. *3.1. Dataset Kelompok Makanan

. ***   Transformasi bentuk komoditi pangan sebanyak 126 komoditi dari vertikal

. ***   menjadi horisontal

. use $datablok41\blok41_35.dta, clear

 

. order r102 r107 r108 kode b41k5 b41k6 urut

 

. keep r102 r107 r108 kode b41k5 b41k6 urut

 

. *Mentransformasikan bentuk data dari vertikal menjadi horisontal

. reshape wide b41k5 b41k6, i(urut) j(kode)

(note: j = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

>  48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 9

> 4 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126)

 

Data                               long   ->   wide

—————————————————————————–

Number of obs.                  1.4e+06   ->   29467

Number of variables                   7   ->     256

j variable (126 values)            kode   ->   (dropped)

xij variables:

b41k5   ->   b41k51 b41k52 … b41k5126

b41k6   ->   b41k61 b41k62 … b41k6126

—————————————————————————–

 

. *     Memberi nama label komoditas

. label variable b41k51 “q1 padi-padian”

 

. label variable b41k61 “p1 padi-padian”

 

. label variable b41k52 “q2 beras”

 

. label variable b41k62 “p2 beras”

 

. label variable b41k53 “q3 beras ketan”

 

. label variable b41k63 “p3 beras ketan”

 

. label variable b41k54 “q4 jagung basah dengan kulit”

 

. label variable b41k64 “p4 jagung basah dengan kulit”

 

. label variable b41k55 “q5 jagung pipilan”

 

. label variable b41k65 “p5 jagung pipilan”

 

. label variable b41k56 “q6 tepung terigu”

 

. label variable b41k66 “p6 tepung terigu”

 

. label variable b41k57 “q7 umbi-umbian”

 

. label variable b41k67 “p7 umbi-umbian”

 

. label variable b41k58 “q8 ubi”

 

. label variable b41k68 “p8 ubi”

 

. label variable b41k59 “q9 singkong”

 

. label variable b41k69 “p9 singkong”

 

. label variable b41k510 “q10 sagu”

 

. label variable b41k610 “p10 sagu”

 

. label variable b41k511 “q11 talas”

 

. label variable b41k611 “p11 talas”

 

. label variable b41k512 “q12 kentang”

 

. label variable b41k612 “p12 kentang”

 

. label variable b41k513 “q13 gaplek”

 

. label variable b41k613 “p13 gaplek”

 

. label variable b41k514 “q14 ikan/udang/cumi/kerang”

 

. label variable b41k614 “p14 ikan/udang/cumi/kerang”

 

. label variable b41k515 “q15 tongkol/tuna”

 

. label variable b41k615 “p15 tongkol/tuna”

 

. label variable b41k516 “q16 kembung”

 

. label variable b41k616 “p16 kembung”

 

. label variable b41k517 “q17 teri”

 

. label variable b41k617 “p17 teri”

 

. label variable b41k518 “q18 mujair”

 

. label variable b41k618 “p18 mujair”

 

. label variable b41k519 “q19 bandeng”

 

. label variable b41k619 “p19 bandeng”

 

. label variable b41k520 “q20 lele”

 

. label variable b41k620 “p20 lele”

 

. label variable b41k521 “q21 ikan tawar segar”

 

. label variable b41k621 “p21 ikan tawar segar”

 

. label variable b41k522 “q22 ikan laut segar”

 

. label variable b41k622 “p22 ikan laut segar”

 

. label variable b41k523 “q23 udang segar”

 

. label variable b41k623 “p23 udang segar”

 

. label variable b41k524 “q24 ikan tawar diawetkan”

 

. label variable b41k624 “p24 ikan tawar diawetkan”

 

. label variable b41k525 “q25 ikan laut diawetkan”

 

. label variable b41k625 “p25 ikan laut diawetkan”

 

. label variable b41k526 “q26 udang diawetkan”

 

. label variable b41k626 “p26 udang diawetkan”

 

. label variable b41k527 “q27 ikan dalam kaleng”

 

. label variable b41k627 “p27 ikan dalam kaleng”

 

. label variable b41k528 “q28 daging”

 

. label variable b41k628 “p28 daging”

 

. label variable b41k529 “q29 daging sapi”

 

. label variable b41k629 “p29 daging sapi”

 

. label variable b41k530 “q30 daging babi”

 

. label variable b41k630 “p30 daging babi”

 

. label variable b41k531 “q31 daging ayam ras”

 

. label variable b41k631 “p31 daging ayam ras”

 

. label variable b41k532 “q32 daging ayam kampung”

 

. label variable b41k632 “p32 daging ayam kampung”

 

. label variable b41k533 “q33 daging diawetkan”

 

. label variable b41k633 “p33 daging diawetkan”

 

. label variable b41k534 “q34 tetelan”

 

. label variable b41k634 “p34 tetelan”

 

. label variable b41k535 “q35 telur dan susu”

 

. label variable b41k635 “p35 telur dan susu”

 

. label variable b41k536 “q36 telur ayam ras”

 

. label variable b41k636 “p36 telur ayam ras”

 

. label variable b41k537 “q37 telur ayam kampung”

 

. label variable b41k637 “p37 telur ayam kampung”

 

. label variable b41k538 “q38 telur itik”

 

. label variable b41k638 “p38 telur itik”

 

. label variable b41k539 “q39 telur puyuh”

 

. label variable b41k639 “p39 telur puyuh”

 

. label variable b41k540 “q40 susu bubuk”

 

. label variable b41k640 “p40 susu bubuk”

 

. label variable b41k541 “q41 susu cair pabrik”

 

. label variable b41k641 “p41 susu cair pabrik”

 

. label variable b41k542 “q42 susu kentaal manis”

 

. label variable b41k642 “p42 susu kental manis”

 

. label variable b41k543 “q43 susu bubuk bayi”

 

. label variable b41k643 “p43 susu bubuk bayi”

 

. label variable b41k544 “q44 sayur-sayuran”

 

. label variable b41k644 “p44 sayur-sayuran”

 

. label variable b41k545 “q45 bayam”

 

. label variable b41k645 “p45 bayam”

 

. label variable b41k546 “q46 kangkung”

 

. label variable b41k646 “p46 kangkung”

 

. label variable b41k547 “q47 sawi hijau”

 

. label variable b41k647 “p47 sawi hijau”

 

. label variable b41k548 “q48 buncis”

 

. label variable b41k648 “p48 buncis”

 

. label variable b41k549 “q49 kacang panjang”

 

. label variable b41k649 “p49 kacang panjang”

 

. label variable b41k550 “q50 tomat”

 

. label variable b41k650 “p50 tomat”

 

. label variable b41k551 “q51 daun ketela pohon”

 

. label variable b41k651 “p51 daun ketela pohon”

 

. label variable b41k552 “q52 terong”

 

. label variable b41k652 “p52 terong”

 

. label variable b41k553 “q53 tauge”

 

. label variable b41k653 “p53 tauge”

 

. label variable b41k554 “q54 sayur sop/capcay”

 

. label variable b41k654 “p54 sayur sop/capcay”

 

. label variable b41k555 “q55 sayur asam/lodeh”

 

. label variable b41k655 “p55 sayur asam/lodeh”

 

. label variable b41k556 “q56 nangka muda”

 

. label variable b41k656 “p56 nangka muda”

 

. label variable b41k557 “q57 bawang merah”

 

. label variable b41k657 “p57 bawang merah”

 

. label variable b41k558 “q58 bawang putih”

 

. label variable b41k658 “p58 bawang putih”

 

. label variable b41k559 “q59 cabe merah”

 

. label variable b41k659 “p59 cabe merah”

 

. label variable b41k560 “q60 cabe rawit”

 

. label variable b41k660 “p60 cabe rawit”

 

. label variable b41k561 “q61 kacang-kacangan ”

 

. label variable b41k661 “p61 kacang-kacangan”

 

. label variable b41k562 “q62 kacang tanah tanpa kulit”

 

. label variable b41k662 “p62 kacang tanah tanpa kulit”

 

. label variable b41k563 “q63 tahu”

 

. label variable b41k663 “p63 tahu”

 

. label variable b41k564 “q64 tempe”

 

. label variable b41k664 “p64 tempe”

 

. label variable b41k565 “q65 buah-buahan”

 

. label variable b41k665 “p65 buah-buahan”

 

. label variable b41k566 “q66 jeruk ”

 

. label variable b41k666 “p66 jeruk”

 

. label variable b41k567 “q67 mangga”

 

. label variable b41k667 “p67 mangga”

 

. label variable b41k568 “q68 apel”

 

. label variable b41k668 “p68 apel”

 

. label variable b41k569 “q69 rambutan”

 

. label variable b41k669 “p69 rambutan”

 

. label variable b41k570 “q70 duku”

 

. label variable b41k670 “p70 duku”

 

. label variable b41k571 “q71 durian”

 

. label variable b41k671 “p71 durian”

 

. label variable b41k572 “q72 salak”

 

. label variable b41k672 “p72 salak”

 

. label variable b41k573 “q73 pisang”

 

. label variable b41k673 “p73 pisang”

 

. label variable b41k574 “q74 pepaya”

 

. label variable b41k674 “p74 pepaya”

 

. label variable b41k575 “q75 semangka”

 

. label variable b41k675 “p75 semangka”

 

. label variable b41k576 “q76 minyak dan kelapa”

 

. label variable b41k676 “p76 minyak dan kelapa”

 

. label variable b41k577 “q77 minyak goreng”

 

. label variable b41k677 “p77 minyak goreng”

 

. label variable b41k578 “q78 minyak kelapa”

 

. label variable b41k678 “p78 minyak kelapa”

 

. label variable b41k579 “q79 kelapa”

 

. label variable b41k679 “p79 kelapa”

 

. label variable b41k580 “q80 bahan minuman”

 

. label variable b41k680 “p80 bahan minuman”

 

. label variable b41k581 “q81 gula pasir”

 

. label variable b41k681 “p81 gula pasir”

 

. label variable b41k582 “q82 gula merah”

 

. label variable b41k682 “p82 gula merah”

 

. label variable b41k583 “q83 teh bubuk”

 

. label variable b41k683 “p83 teh bubuk”

 

. label variable b41k584 “q84 teh celup”

 

. label variable b41k684 “p84 teh celup”

 

. label variable b41k585 “q85 kopi bubuk”

 

. label variable b41k685 “p85 kopi bubuk”

 

. label variable b41k586 “q86 kopi instan”

 

. label variable b41k686 “p86 kopi instan”

 

. label variable b41k587 “q87 bumbu-bumbuan”

 

. label variable b41k687 “p87 bumbu-bumbuan”

 

. label variable b41k588 “q88 garam”

 

. label variable b41k688 “p88 garam”

 

. label variable b41k589 “q89 kemiri”

 

. label variable b41k689 “p89 kemiri”

 

. label variable b41k590 “q90 ketumbar”

 

. label variable b41k690 “p90 ketumbar”

 

. label variable b41k591 “q91 merica”

 

. label variable b41k691 “p91 merica”

 

. label variable b41k592 “q92 asam”

 

. label variable b41k692 “p92 asam”

 

. label variable b41k593 “q93 terasi/petis”

 

. label variable b41k693 “p93 terasi/petis”

 

. label variable b41k594 “q94 kecap”

 

. label variable b41k694 “p94 kecap”

 

. label variable b41k595 “q95 vetsin”

 

. label variable b41k695 “p95 vetsin”

 

. label variable b41k596 “q96 bumbu masak instan”

 

. label variable b41k696 “p96 bumbu masak instan”

 

. label variable b41k597 “q97 bumbu lainnya”

 

. label variable b41k697 “p97 bumbu lainnya”

 

. label variable b41k598 “q98 konsumsi lainnya”

 

. label variable b41k698 “p98 konsumsi lainnya”

 

. label variable b41k599 “q99 mie instan”

 

. label variable b41k699 “p99 mie instan”

 

. label variable b41k5100 “q100 kerupuk mentah”

 

. label variable b41k6100 “p100 kerupuk mentah”

 

. label variable b41k5101 “q101 bubur bayi kemasan”

 

. label variable b41k6101 “p101 bubur bayi kemasan”

 

. label variable b41k5102 “q102 makanan dan minuman jadi”

 

. label variable b41k6102 “p102 makanan dan minuman jadi”

 

. label variable b41k5103 “q103 roti”

 

. label variable b41k6103 “p103 roti”

 

. label variable b41k5104 “q104 kue kering/biskuit”

 

. label variable b41k6104 “p104 kue kering/biskuit”

 

. label variable b41k5105 “q105 kue basah”

 

. label variable b41k6105 “p105 kue basah”

 

. label variable b41k5106 “q106 makanan gorengan”

 

. label variable b41k6106 “p106 makanan gorengan”

 

. label variable b41k5107 “q107 gado-gado”

 

. label variable b41k6107 “p107 gado-gado”

 

. label variable b41k5108 “q108 nasi campur”

 

. label variable b41k6108 “p108 nasi campur”

 

. label variable b41k5109 “q109 nasi goreng”

 

. label variable b41k6109 “p109 nasi goreng”

 

. label variable b41k5110 “q110 nasi putih”

 

. label variable b41k6110 “p110 nasi putih”

 

. label variable b41k5111 “q111 ketupat sayur”

 

. label variable b41k6111 “p111 ketupat sayur”

 

. label variable b41k5112 “q112 soto/gule/rawon/cincang”

 

. label variable b41k6112 “p112 soto/gule/rawon/cincang”

 

. label variable b41k5113 “q113 mie bakso/mie rebus/mie goreng”

 

. label variable b41k6113 “p113 mie bakso/mie rebus/mie goreng”

 

. label variable b41k5114 “q114 makanan ringan/krupuk”

 

. label variable b41k6114 “p114 makanan ringan/krupuk”

 

. label variable b41k5115 “q115 ikan goreng/bakar/presto/pindang/pepes”

 

. label variable b41k6115 “p115 ikan goreng/bakar/presto/pindang/pepes”

 

. label variable b41k5116 “q116 ayam/daging goreng/bakar/rendang/sate”

 

. label variable b41k6116 “p116 ayam/daging goreng/bakar/rendang/sate”

 

. label variable b41k5117 “q117 air kemasan”

 

. label variable b41k6117 “p117 air kemasan”

 

. label variable b41k5118 “q118 air kemasan galon”

 

. label variable b41k6118 “p118 air kemasan galon”

 

. label variable b41k5119 “q119 es ”

 

. label variable b41k6119 “p119 es”

 

. label variable b41k5120 “q120 minuman bersoda”

 

. label variable b41k6120 “p120 minuman bersoda”

 

. label variable b41k5121 “q121 minuman jadi”

 

. label variable b41k6121 “p121 minuman jadi”

 

. label variable b41k5122 “q122 minuman keras”

 

. label variable b41k6122 “p122 minuman keras”

 

. label variable b41k5123 “q123 rokok”

 

. label variable b41k6123 “p123 rokok”

 

. label variable b41k5124 “q124 rokok kretek tanpa filter”

 

. label variable b41k6124 “p124 rokok kretek tanpa filter”

 

. label variable b41k5125 “q125 rokok kretek filter”

 

. label variable b41k6125 “p125 rokok kretek filter”

 

. label variable b41k5126 “q126 rokok putih”

 

. label variable b41k6126 “p126 rokok putih”

 

. * mengganti nama kode

. rename b41k51 q1

 

. rename b41k61 p1

 

. rename b41k52 q2

 

. rename b41k62 p2

 

. rename b41k53 q3

 

. rename b41k63 p3

 

. rename b41k54 q4

 

. rename b41k64 p4

 

. rename b41k55 q5

 

. rename b41k65 p5

 

. rename b41k56 q6

 

. rename b41k66 p6

 

. rename b41k57 q7

 

. rename b41k67 p7

 

. rename b41k58 q8

 

. rename b41k68 p8

 

. rename b41k59 q9

 

. rename b41k69 p9

 

. rename b41k510 q10

 

. rename b41k610 p10

 

. rename b41k511 q11

 

. rename b41k611 p11

 

. rename b41k512 q12

 

. rename b41k612 p12

 

. rename b41k513 q13

 

. rename b41k613 p13

 

. rename b41k514 q14

 

. rename b41k614 p14

 

. rename b41k515 q15

 

. rename b41k615 p15

 

. rename b41k516 q16

 

. rename b41k616 p16

 

. rename b41k517 q17

 

. rename b41k617 p17

 

. rename b41k518 q18

 

. rename b41k618 p18

 

. rename b41k519 q19

 

. rename b41k619 p19

 

. rename b41k520 q20

 

. rename b41k620 p20

 

. rename b41k521 q21

 

. rename b41k621 p21

 

. rename b41k522 q22

 

. rename b41k622 p22

 

. rename b41k523 q23

 

. rename b41k623 p23

 

. rename b41k524 q24

 

. rename b41k624 p24

 

. rename b41k525 q25

 

. rename b41k625 p25

 

. rename b41k526 q26

 

. rename b41k626 p26

 

. rename b41k527 q27

 

. rename b41k627 p27

 

. rename b41k528 q28

 

. rename b41k628 p28

 

. rename b41k529 q29

 

. rename b41k629 p29

 

. rename b41k530 q30

 

. rename b41k630 p30

 

. rename b41k531 q31

 

. rename b41k631 p31

 

. rename b41k532 q32

 

. rename b41k632 p32

 

. rename b41k533 q33

 

. rename b41k633 p33

 

. rename b41k534 q34

 

. rename b41k634 p34

 

. rename b41k535 q35

 

. rename b41k635 p35

 

. rename b41k536 q36

 

. rename b41k636 p36

 

. rename b41k537 q37

 

. rename b41k637 p37

 

. rename b41k538 q38

 

. rename b41k638 p38

 

. rename b41k539 q39

 

. rename b41k639 p39

 

. rename b41k540 q40

 

. rename b41k640 p40

 

. rename b41k541 q41

 

. rename b41k641 p41

 

. rename b41k542 q42

 

. rename b41k642 p42

 

. rename b41k543 q43

 

. rename b41k643 p43

 

. rename b41k544 q44

 

. rename b41k644 p44

 

. rename b41k545 q45

 

. rename b41k645 p45

 

. rename b41k546 q46

 

. rename b41k646 p46

 

. rename b41k547 q47

 

. rename b41k647 p47

 

. rename b41k548 q48

 

. rename b41k648 p48

 

. rename b41k549 q49

 

. rename b41k649 p49

 

. rename b41k550 q50

 

. rename b41k650 p50

 

. rename b41k551 q51

 

. rename b41k651 p51

 

. rename b41k552 q52

 

. rename b41k652 p52

 

. rename b41k553 q53

 

. rename b41k653 p53

 

. rename b41k554 q54

 

. rename b41k654 p54

 

. rename b41k555 q55

 

. rename b41k655 p55

 

. rename b41k556 q56

 

. rename b41k656 p56

 

. rename b41k557 q57

 

. rename b41k657 p57

 

. rename b41k558 q58

 

. rename b41k658 p58

 

. rename b41k559 q59

 

. rename b41k659 p59

 

. rename b41k560 q60

 

. rename b41k660 p60

 

. rename b41k561 q61

 

. rename b41k661 p61

 

. rename b41k562 q62

 

. rename b41k662 p62

 

. rename b41k563 q63

 

. rename b41k663 p63

 

. rename b41k564 q64

 

. rename b41k664 p64

 

. rename b41k565 q65

 

. rename b41k665 p65

 

. rename b41k566 q66

 

. rename b41k666 p66

 

. rename b41k567 q67

 

. rename b41k667 p67

 

. rename b41k568 q68

 

. rename b41k668 p68

 

. rename b41k569 q69

 

. rename b41k669 p69

 

. rename b41k570 q70

 

. rename b41k670 p70

 

. rename b41k571 q71

 

. rename b41k671 p71

 

. rename b41k572 q72

 

. rename b41k672 p72

 

. rename b41k573 q73

 

. rename b41k673 p73

 

. rename b41k574 q74

 

. rename b41k674 p74

 

. rename b41k575 q75

 

. rename b41k675 p75

 

. rename b41k576 q76

 

. rename b41k676 p76

 

. rename b41k577 q77

 

. rename b41k677 p77

 

. rename b41k578 q78

 

. rename b41k678 p78

 

. rename b41k579 q79

 

. rename b41k679 p79

 

. rename b41k580 q80

 

. rename b41k680 p80

 

. rename b41k581 q81

 

. rename b41k681 p81

 

. rename b41k582 q82

 

. rename b41k682 p82

 

. rename b41k583 q83

 

. rename b41k683 p83

 

. rename b41k584 q84

 

. rename b41k684 p84

 

. rename b41k585 q85

 

. rename b41k685 p85

 

. rename b41k586 q86

 

. rename b41k686 p86

 

. rename b41k587 q87

 

. rename b41k687 p87

 

. rename b41k588 q88

 

. rename b41k688 p88

 

. rename b41k589 q89

 

. rename b41k689 p89

 

. rename b41k590 q90

 

. rename b41k690 p90

 

. rename b41k591 q91

 

. rename b41k691 p91

 

. rename b41k592 q92

 

. rename b41k692 p92

 

. rename b41k593 q93

 

. rename b41k693 p93

 

. rename b41k594 q94

 

. rename b41k694 p94

 

. rename b41k595 q95

 

. rename b41k695 p95

 

. rename b41k596 q96

 

. rename b41k696 p96

 

. rename b41k597 q97

 

. rename b41k697 p97

 

. rename b41k598 q98

 

. rename b41k698 p98

 

. rename b41k599 q99

 

. rename b41k699 p99

 

. rename b41k5100 q100

 

. rename b41k6100 p100

 

. rename b41k5101 q101

 

. rename b41k6101 p101

 

. rename b41k5102 q102

 

. rename b41k6102 p102

 

. rename b41k5103 q103

 

. rename b41k6103 p103

 

. rename b41k5104 q104

 

. rename b41k6104 p104

 

. rename b41k5105 q105

 

. rename b41k6105 p105

 

. rename b41k5106 q106

 

. rename b41k6106 p106

 

. rename b41k5107 q107

 

. rename b41k6107 p107

 

. rename b41k5108 q108

 

. rename b41k6108 p108

 

. rename b41k5109 q109

 

. rename b41k6109 p109

 

. rename b41k5110 q110

 

. rename b41k6110 p110

 

. rename b41k5111 q111

 

. rename b41k6111 p111

 

. rename b41k5112 q112

 

. rename b41k6112 p112

 

. rename b41k5113 q113

 

. rename b41k6113 p113

 

. rename b41k5114 q114

 

. rename b41k6114 p114

 

. rename b41k5115 q115

 

. rename b41k6115 p115

 

. rename b41k5116 q116

 

. rename b41k6116 p116

 

. rename b41k5117 q117

 

. rename b41k6117 p117

 

. rename b41k5118 q118

 

. rename b41k6118 p118

 

. rename b41k5119 q119

 

. rename b41k6119 p119

 

. rename b41k5120 q120

 

. rename b41k6120 p120

 

. rename b41k5121 q121

 

. rename b41k6121 p121

 

. rename b41k5122 q122

 

. rename b41k6122 p122

 

. rename b41k5123 q123

 

. rename b41k6123 p123

 

. rename b41k5124 q124

 

. rename b41k6124 p124

 

. rename b41k5125 q125

 

. rename b41k6125 p125

 

. rename b41k5126 q126

 

. rename b41k6126 p126

 

. rename r108 ruta

 

. rename r102 kab

 

. rename r107 nks

 

. sort urut

 

. count

29467

 

. tempfile data41

 

. save `data41′, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_05000009.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_05000009.tmp saved

 

. save $dataproses\data41.dta, replace

file D:\tesis_dewi_aids\running\02_dataproses\\data41.dta saved

 

.

. *3.2. Pengelompokan Komoditas Pangan Penelitian

. ***   126 Komoditi dalam Susenas 2016 diklasifikakan ke dalam 6 kelompok yaitu :

. **    1. Padi-padian dan umbi-umbian

. **    2. Ikan/Daging/Telur/Susu

. **    3. Sayur dan buah-buahan

. **    4. Kacang dan Minyak

. **    5. Makanan Jadi dan Rokok

. **    6. Bahan pangan lain

.

 

 

. *3.2.1. Dataset Kelompok 1. Padi dan umbi-umbian

. * Terdiri dari 11 komoditi pangan (kode 2-6 dan kode 8-13)

. use $dataproses\data41.dta, clear

 

. order urut kab nks ruta q2 p2 q3 p3 q4 p4 q5 p5 q6 p6 q8 p8 q9 p9 q10 p10 q11 p11 q12 p12 q13 p13

 

. keep urut kab nks ruta q2 p2 q3 p3 q4 p4 q5 p5 q6 p6 q8 p8 q9 p9 q10 p10 q11 p11 q12 p12 q13 p13

 

. for var q2 q3 q4 q5 q6 q8 q9 q10 q11 q12 q13 : replace X=0 if X==.

 

->  replace q2=0 if q2==.

(763 real changes made)

 

->  replace q3=0 if q3==.

(29207 real changes made)

 

->  replace q4=0 if q4==.

(25666 real changes made)

 

->  replace q5=0 if q5==.

(24973 real changes made)

 

->  replace q6=0 if q6==.

(21250 real changes made)

 

->  replace q8=0 if q8==.

(25174 real changes made)

 

->  replace q9=0 if q9==.

(23619 real changes made)

 

->  replace q10=0 if q10==.

(29430 real changes made)

 

->  replace q11=0 if q11==.

(29233 real changes made)

 

->  replace q12=0 if q12==.

(23153 real changes made)

 

->  replace q13=0 if q13==.

(28886 real changes made)

 

. for var p2 p3 p4 p5 p6 p8 p9 p10 p11 p12 p13 : replace X=0 if X==.

 

->  replace p2=0 if p2==.

(763 real changes made)

 

->  replace p3=0 if p3==.

(29207 real changes made)

 

->  replace p4=0 if p4==.

(25666 real changes made)

 

->  replace p5=0 if p5==.

(24973 real changes made)

 

->  replace p6=0 if p6==.

(21250 real changes made)

 

->  replace p8=0 if p8==.

(25174 real changes made)

 

->  replace p9=0 if p9==.

(23619 real changes made)

 

->  replace p10=0 if p10==.

(29430 real changes made)

 

->  replace p11=0 if p11==.

(29233 real changes made)

 

->  replace p12=0 if p12==.

(23153 real changes made)

 

->  replace p13=0 if p13==.

(28886 real changes made)

 

. * Hitung unit value, unit value rata-rata dan ln deviasi harga

. gen kuantitas1_minggu=(q2+q3+q4+q5+q6+q8+q9+q10+q11+q12+q13)

 

. gen kuantitas1bln = ((kuantitas1_minggu)*(30/7))

 

. gen harga1_minggu = (p2+ p3+ p4+ p5+ p6+ p8+ p9+ p10+ p11+ p12+ p13)

 

. gen harga1bln = ((harga1_minggu)*(30/7))

 

. gen pi1 = (harga1bln/ kuantitas1bln)

(729 missing values generated)

 

. replace pi1=0 if pi1==.

(729 real changes made)

 

. gen Lnpi1 = ln(1+pi1)

 

. *Membuat variabel baru dari rata-rata suatu variabel dan berdasarkan variabel lainnya

. bys kab nks: egen pi1mean=mean(pi1)

 

. gen Lnpi1mean = ln(1+pi1mean)

 

. gen LnDev1 = (Lnpi1-Lnpi1mean)

 

. label variable LnDev1 “Ln deviasi unit value kelompok 1”

 

. label variable kuantitas1_minggu “total kuantitas kelompok 1 seminggu”

 

. label variable kuantitas1bln “total kuantitas kelompok 1 sebulan”

 

. label variable harga1_minggu “total harga kelompok 1 seminggu”

 

. label variable harga1bln “total harga kelompok 1 sebulan”

 

. label variable pi1 “unit value kelompok 1”

 

. label variable Lnpi1 “Ln unit value kelompok 1”

 

. label variable pi1mean “rata-rata unit value kelompok 1”

 

. label variable Lnpi1mean “Ln rata-rata unit value kelompok 1”

 

. keep urut kuantitas1bln harga1bln pi1 Lnpi1 pi1mean Lnpi1mean LnDev1

 

. count

29467

 

. sort urut

 

. tempfile kelompok1

 

. save `kelompok1′, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_0500000a.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_0500000a.tmp saved

 

. save $dataproses\kelompok1.dta, replace

file D:\tesis_dewi_aids\running\02_dataproses\\kelompok1.dta saved

 

.

. *3.2.2. Dataset Kelompok 2. Ikan, Daging, Telur dan Susu

. * Terdiri dari 27 komoditi pangan (kode 15-27, kode 29-34, kode 36-43)

. use $dataproses\data41.dta, clear

 

. order urut kab nks ruta q15 p15 q16 p16 q17 p17 q18 p18 q19 p19 q20 p20 q21 p21 q22 p22 q23 p23 q24 p24 q25 p25 q26 p26 q27 p27 q29 p29 q30

> p30 q31 p31 q32 p32 q33 p33 q34 p34 q36 p36 q37 p37 q38 p38 q39 p39 q40 p40 q41 p41 q42 p42 q43 p43

 

. keep urut kab nks ruta q15 p15 q16 p16 q17 p17 q18 p18 q19 p19 q20 p20 q21 p21 q22 p22 q23 p23 q24 p24 q25 p25 q26 p26 q27 p27 q29 p29 q30 p

> 30 q31 p31 q32 p32 q33 p33 q34 p34 q36 p36 q37 p37 q38 p38 q39 p39 q40 p40 q41 p41 q42 p42 q43 p43

 

 

 

. for var q15 q16 q17 q18 q19 q20 q21 q22 q23 q24 q25 q26 q27 q29 q30 q31 q32 ///

>           q33 q34 q36 q37 q38 q39 q40 q41 q42 q43 : replace X=0 if X==.

 

->  replace q15=0 if q15==.

(19868 real changes made)

 

->  replace q16=0 if q16==.

(28457 real changes made)

 

->  replace q17=0 if q17==.

(25501 real changes made)

 

->  replace q18=0 if q18==.

(25380 real changes made)

 

->  replace q19=0 if q19==.

(25320 real changes made)

 

->  replace q20=0 if q20==.

(22979 real changes made)

 

->  replace q21=0 if q21==.

(28055 real changes made)

 

->  replace q22=0 if q22==.

(23561 real changes made)

 

->  replace q23=0 if q23==.

(25290 real changes made)

 

->  replace q24=0 if q24==.

(28383 real changes made)

 

->  replace q25=0 if q25==.

(18895 real changes made)

 

->  replace q26=0 if q26==.

(29001 real changes made)

 

->  replace q27=0 if q27==.

(29258 real changes made)

 

->  replace q29=0 if q29==.

(26053 real changes made)

 

->  replace q30=0 if q30==.

(29402 real changes made)

 

->  replace q31=0 if q31==.

(16183 real changes made)

 

->  replace q32=0 if q32==.

(28091 real changes made)

 

->  replace q33=0 if q33==.

(28223 real changes made)

 

->  replace q34=0 if q34==.

(28854 real changes made)

 

->  replace q36=0 if q36==.

(5358 real changes made)

 

->  replace q37=0 if q37==.

(26825 real changes made)

 

->  replace q38=0 if q38==.

(29000 real changes made)

 

->  replace q39=0 if q39==.

(28395 real changes made)

 

->  replace q40=0 if q40==.

(26461 real changes made)

 

->  replace q41=0 if q41==.

(28076 real changes made)

 

->  replace q42=0 if q42==.

(25627 real changes made)

 

->  replace q43=0 if q43==.

(27959 real changes made)

 

. for var p15 p16 p17 p18 p19 p20 p21 p22 p23 p24 p25 p26 p27 p29 p30 p31 p32 ///

>           p33 p34 p36 p37 p38 p39 p40 p41 p42 p43 : replace X=0 if X==.

 

->  replace p15=0 if p15==.

(19868 real changes made)

 

->  replace p16=0 if p16==.

(28457 real changes made)

 

->  replace p17=0 if p17==.

(25501 real changes made)

 

->  replace p18=0 if p18==.

(25380 real changes made)

 

->  replace p19=0 if p19==.

(25320 real changes made)

 

->  replace p20=0 if p20==.

(22979 real changes made)

 

->  replace p21=0 if p21==.

(28055 real changes made)

 

->  replace p22=0 if p22==.

(23561 real changes made)

 

->  replace p23=0 if p23==.

(25290 real changes made)

 

->  replace p24=0 if p24==.

(28383 real changes made)

 

->  replace p25=0 if p25==.

(18895 real changes made)

 

->  replace p26=0 if p26==.

(29001 real changes made)

 

->  replace p27=0 if p27==.

(29258 real changes made)

 

->  replace p29=0 if p29==.

(26053 real changes made)

 

->  replace p30=0 if p30==.

(29402 real changes made)

 

->  replace p31=0 if p31==.

(16183 real changes made)

 

->  replace p32=0 if p32==.

(28091 real changes made)

 

->  replace p33=0 if p33==.

(28223 real changes made)

 

->  replace p34=0 if p34==.

(28854 real changes made)

 

->  replace p36=0 if p36==.

(5358 real changes made)

 

->  replace p37=0 if p37==.

(26825 real changes made)

 

->  replace p38=0 if p38==.

(29000 real changes made)

 

 

 

->  replace p39=0 if p39==.

(28395 real changes made)

 

->  replace p40=0 if p40==.

(26461 real changes made)

 

->  replace p41=0 if p41==.

(28076 real changes made)

 

->  replace p42=0 if p42==.

(25627 real changes made)

 

->  replace p43=0 if p43==.

(27959 real changes made)

 

. *** Konversi satuan Quantity komoditas

. **  ons ke kg , 1 ons = 0.1 kg

. gen qkom24=(q24*0.1)

 

. replace q24=qkom24

(1084 real changes made)

 

. gen qkom25=(q25*0.1)

 

. replace q25=qkom25

(10572 real changes made)

 

. gen qkom26=(q26*0.1)

 

. replace q26=qkom26

(466 real changes made)

 

. gen qkom27=(q27*0.1)

 

. replace q27=qkom27

(209 real changes made)

 

. **  butir telur ke kg, 15 butir = 1 kg

. gen qkom36 = (q36/15)

 

. replace q36=qkom36

(24109 real changes made)

 

. gen qkom37 = (q37/15)

 

. replace q37=qkom37

(2642 real changes made)

 

. gen qkom38 = (q38/15)

 

. replace q38=qkom38

(467 real changes made)

 

. **  butir telur puyuh ke kg 100 butir = 1 kg

. gen qkom39 = (q39/100)

 

. replace q39=qkom39

(1072 real changes made)

 

. **  250 ml ke kilo, 250 ml = 250/1000liter = 0.25 kg, asumsi 1 L = 1 Kg

. gen qkom41=(q41*0.25)

 

. replace q41=qkom41

(1391 real changes made)

 

. **  397 gram ke kilo, 397 gr = 0.397 kg

. gen qkom42=(q42*0.397)

 

. replace q42=qkom42

(3840 real changes made)

 

. * Hitung unit value, unit value rata-rata dan ln deviasi harga

. gen kuantitas2_minggu=(q15+q16+q17+q18+q19+q20+q21+q22+q23+q24+q25+q26+q27+q29 ///

>                                    +q30+q31+q32+q33+q34+q36+q37+q38+q39+q40+q41+q42+q43)

 

 

 

. gen kuantitas2bln = ((kuantitas2_minggu)*(30/7))

 

. gen harga2_minggu=(p15+p16+p17+p18+p19+p20+p21+p22+p23+p24+p25+p26+p27+p29 ///

>                                    +p30+p31+p32+p33+p34+p36+p37+p38+p39+p40+p41+p42+p43)

 

. gen harga2bln = ((harga2_minggu)*(30/7))

 

. gen pi2 = (harga2bln/ kuantitas2bln)

(1467 missing values generated)

 

. replace pi2=0 if pi2==.

(1467 real changes made)

 

. gen Lnpi2 = ln(1+pi2)

 

. bys kab nks: egen pi2mean=mean(pi2)

 

. gen Lnpi2mean = ln(1+pi2mean)

 

. gen LnDev2 = (Lnpi2-Lnpi2mean)

 

. label variable LnDev2 “Ln deviasi unit value kelompok 2”

 

. label variable kuantitas2_minggu “total kuantitas kelompok 2 seminggu”

 

. label variable kuantitas2bln “total kuantitas kelompok 2 sebulan”

 

. label variable harga2_minggu “total harga kelompok 2 seminggu”

 

. label variable harga2bln “total harga kelompok 2 sebulan”

 

. label variable pi2 “unit value kelompok 2”

 

. label variable Lnpi2 “Ln unit value kelompok 2”

 

. label variable pi2mean “rata-rata unit value kelompok 2”

 

. label variable Lnpi2mean “Ln rata-rata unit value kelompok 2”

 

. keep urut kuantitas2bln harga2bln pi2 Lnpi2 pi2mean Lnpi2mean LnDev2

 

. count

29467

 

. sort urut

 

. tempfile kelompok2

 

. save `kelompok2′, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_0500000b.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_0500000b.tmp saved

 

.

. *3.2.3. Dataset Kelompok 3. Sayur dan buah-buahan

. * Terdiri dari 16 komoditi pangan (kode 45-60 dan kode 66-75)

. use $dataproses\data41.dta, clear

 

. order urut kab nks ruta q45 p45 q46 p46 q47 p47 q48 p48 q49 p49 q50 p50 q51 p51 q52 p52 q53 p53 q54 p54 q55 p55 q56 p56 q57 p57 q58 p58 q59

> p59 q60 p60 q66 p66 q67 p67 q68 p68 q69 p69 q70 p70 q71 p71 q72 p72 q73 p73 q74 p74 q75 p75

 

. keep urut kab nks ruta q45 p45 q46 p46 q47 p47 q48 p48 q49 p49 q50 p50 q51 p51 q52 p52 q53 p53 q54 p54 q55 p55 q56 p56 q57 p57 q58 p58 q59 p

> 59 q60 p60 q66 p66 q67 p67 q68 p68 q69 p69 q70 p70 q71 p71 q72 p72 q73 p73 q74 p74 q75 p75

 

. for var q45 q46 q47 q48 q49 q50 q51 q52 q53 q54 q55 q56 q57 q58 q59 q60 q66 ///

>           q67 q68 q69 q70 q71 q72 q73 q74 q75 : replace X=0 if X==.

 

->  replace q45=0 if q45==.

(12112 real changes made)

 

->  replace q46=0 if q46==.

(15407 real changes made)

 

->  replace q47=0 if q47==.

(20784 real changes made)

 

->  replace q48=0 if q48==.

(25914 real changes made)

 

->  replace q49=0 if q49==.

(13107 real changes made)

 

->  replace q50=0 if q50==.

(8316 real changes made)

 

->  replace q51=0 if q51==.

(23652 real changes made)

 

->  replace q52=0 if q52==.

(16710 real changes made)

 

->  replace q53=0 if q53==.

(22878 real changes made)

 

->  replace q54=0 if q54==.

(14188 real changes made)

 

->  replace q55=0 if q55==.

(24067 real changes made)

 

->  replace q56=0 if q56==.

(27906 real changes made)

 

->  replace q57=0 if q57==.

(2839 real changes made)

 

->  replace q58=0 if q58==.

(2215 real changes made)

 

->  replace q59=0 if q59==.

(16139 real changes made)

 

->  replace q60=0 if q60==.

(3348 real changes made)

 

->  replace q66=0 if q66==.

(25230 real changes made)

 

->  replace q67=0 if q67==.

(29280 real changes made)

 

->  replace q68=0 if q68==.

(26284 real changes made)

 

->  replace q69=0 if q69==.

(23843 real changes made)

 

->  replace q70=0 if q70==.

(29118 real changes made)

 

->  replace q71=0 if q71==.

(28361 real changes made)

 

->  replace q72=0 if q72==.

(24694 real changes made)

 

->  replace q73=0 if q73==.

(21162 real changes made)

 

->  replace q74=0 if q74==.

(25243 real changes made)

 

->  replace q75=0 if q75==.

(27091 real changes made)

 

. for var p45 p46 p47 p48 p49 p50 p51 p52 p53 p54 p55 p56 p57 p58 p59 p60 p66 ///

>           p67 p68 p69 p70 p71 p72 p73 p74 p75 : replace X=0 if X==.

 

->  replace p45=0 if p45==.

(12112 real changes made)

 

->  replace p46=0 if p46==.

(15407 real changes made)

->  replace p47=0 if p47==.

(20784 real changes made)

 

->  replace p48=0 if p48==.

(25914 real changes made)

 

->  replace p49=0 if p49==.

(13107 real changes made)

 

->  replace p50=0 if p50==.

(8316 real changes made)

 

->  replace p51=0 if p51==.

(23652 real changes made)

 

->  replace p52=0 if p52==.

(16710 real changes made)

 

->  replace p53=0 if p53==.

(22878 real changes made)

 

->  replace p54=0 if p54==.

(14188 real changes made)

 

->  replace p55=0 if p55==.

(24067 real changes made)

 

->  replace p56=0 if p56==.

(27906 real changes made)

 

->  replace p57=0 if p57==.

(2839 real changes made)

 

->  replace p58=0 if p58==.

(2215 real changes made)

 

->  replace p59=0 if p59==.

(16139 real changes made)

 

->  replace p60=0 if p60==.

(3348 real changes made)

 

->  replace p66=0 if p66==.

(25230 real changes made)

 

->  replace p67=0 if p67==.

(29280 real changes made)

 

->  replace p68=0 if p68==.

(26284 real changes made)

 

->  replace p69=0 if p69==.

(23843 real changes made)

 

->  replace p70=0 if p70==.

(29118 real changes made)

 

->  replace p71=0 if p71==.

(28361 real changes made)

 

->  replace p72=0 if p72==.

(24694 real changes made)

 

->  replace p73=0 if p73==.

(21162 real changes made)

 

->  replace p74=0 if p74==.

(25243 real changes made)

 

->  replace p75=0 if p75==.

(27091 real changes made)

 

. *** Konversi satuan Quantity komoditas

. **  bungkus sayur ke kg , asumsi 1 kg = 5 bungkus

. gen qkom54=(q54/5)

 

 

 

. replace q54=qkom54

(15279 real changes made)

 

. gen qkom55=(q55/5)

 

. replace q55=qkom55

(5400 real changes made)

 

. **  ons ke kg , 1 ons = 0.1 kg

. gen qkom57=(q57*0.1)

 

. replace q57=qkom57

(26628 real changes made)

 

. gen qkom58=(q58*0.1)

 

. replace q58=qkom58

(27252 real changes made)

 

. * Hitung unit value, unit value rata-rata dan ln deviasi harga

. gen kuantitas3_minggu=(q45+q46+q47+q48+q49+q50+q51+q52+q53+q54+q55+q56+q57 ///

>                                         +q58+q59+q60+q66+q67+q68+q69+q70+q71+q72+q73+q74+q75)

 

. gen kuantitas3bln = ((kuantitas3_minggu)*(30/7))

 

. gen harga3_minggu=(p45+p46+p47+p48+p49+p50+p51+p52+p53+p54+p55+p56+p57+p58 ///

>                                         +p59+p60+p66+p67+p68+p69+p70+p71+p72+p73+p74+p75)

 

. gen harga3bln = ((harga3_minggu)*(30/7))

 

. gen pi3 = (harga3bln/ kuantitas3bln)

(826 missing values generated)

 

. replace pi3=0 if pi3==.

(826 real changes made)

 

. gen Lnpi3 = ln(1+pi3)

 

. bys kab nks: egen pi3mean=mean(pi3)

 

. gen Lnpi3mean = ln(1+pi3mean)

 

. gen LnDev3 = (Lnpi3-Lnpi3mean)

 

. label variable LnDev3 “Ln deviasi unit value kelompok 3”

 

. label variable kuantitas3_minggu “total kuantitas kelompok 3 seminggu”

 

. label variable kuantitas3bln “total kuantitas kelompok 3 sebulan”

 

. label variable harga3_minggu “total harga kelompok 3 seminggu”

 

. label variable harga3bln “total harga kelompok 3 sebulan”

 

. label variable pi3 “unit value kelompok 3”

 

. label variable Lnpi3 “Ln unit value kelompok 3”

 

. label variable pi3mean “rata-rata unit value kelompok 3”

 

. label variable Lnpi3mean “Ln rata-rata unit value kelompok 3”

 

. keep urut kuantitas3bln harga3bln pi3 Lnpi3 pi3mean Lnpi3mean LnDev3

 

. count

29467

 

. sort urut

 

. tempfile kelompok3

 

. save `kelompok3′, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_0500000c.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_0500000c.tmp saved

 

 

. *3.2.4. Dataset Kelompok 4. Kacang dan Minyak

. * Terdiri dari 6 komoditi pangan (kode 62-64 dan kode 77-79)

. use $dataproses\data41.dta, clear

 

. order urut kab nks ruta q62 p62 q63 p63 q64 p64 q77 p77 q78 p78 q79 p79

 

. keep urut kab nks ruta q62 p62 q63 p63 q64 p64 q77 p77 q78 p78 q79 p79

 

. for var q62 q63 q64 q77 q78 q79 : replace X=0 if X==.

 

->  replace q62=0 if q62==.

(26424 real changes made)

 

->  replace q63=0 if q63==.

(3305 real changes made)

 

->  replace q64=0 if q64==.

(3625 real changes made)

 

->  replace q77=0 if q77==.

(1281 real changes made)

 

->  replace q78=0 if q78==.

(29223 real changes made)

 

->  replace q79=0 if q79==.

(15587 real changes made)

 

. for var p62 p63 p64 p77 p78 p79 : replace X=0 if X==.

 

->  replace p62=0 if p62==.

(26424 real changes made)

 

->  replace p63=0 if p63==.

(3305 real changes made)

 

->  replace p64=0 if p64==.

(3625 real changes made)

 

->  replace p77=0 if p77==.

(1281 real changes made)

 

->  replace p78=0 if p78==.

(29223 real changes made)

 

->  replace p79=0 if p79==.

(15587 real changes made)

 

. *** Konversi satuan Quantity komoditas

. **  liter ke kg , asumsi 1 kg = 1 liter

. gen qkom77=(q77*1)

 

. replace q77=qkom77

(0 real changes made)

 

. gen qkom78=(q78*1)

 

. replace q78=qkom78

(0 real changes made)

 

. **  butir kelapa ke kg , 1 butir = 0,9 liter = 0,9 kg

. gen qkom79=(q79*0.9)

 

. replace q79=qkom79

(13880 real changes made)

 

. * Hitung unit value, unit value rata-rata dan ln deviasi harga

. gen kuantitas4_minggu=(q62+q63+q64+q77+q78+q79)

 

. gen kuantitas4bln = ((kuantitas4_minggu)*(30/7))

 

. gen harga4_minggu = (p62+p63+p64+p77+p78+p79)

 

. gen harga4bln = ((harga4_minggu)*(30/7))

 

. gen pi4 = (harga4bln/ kuantitas4bln)

(1009 missing values generated)

. replace pi4=0 if pi4==.

(1009 real changes made)

 

. gen Lnpi4 = ln(1+pi4)

 

. bys kab nks: egen pi4mean=mean(pi4)

 

. gen Lnpi4mean = ln(1+pi4mean)

 

. gen LnDev4 = (Lnpi4-Lnpi4mean)

 

. label variable LnDev4 “Ln deviasi unit value kelompok 4”

 

. label variable kuantitas4_minggu “total kuantitas kelompok 4 seminggu”

 

. label variable kuantitas4bln “total kuantitas kelompok 4 sebulan”

 

. label variable harga4_minggu “total harga kelompok 4 seminggu”

 

. label variable harga4bln “total harga kelompok 4 sebulan”

 

. label variable pi4 “unit value kelompok 4”

 

. label variable Lnpi4 “Ln unit value kelompok 4”

 

. label variable pi4mean “rata-rata unit value kelompok 4”

 

. label variable Lnpi4mean “Ln rata-rata unit value kelompok 4”

 

. keep urut kuantitas4bln harga4bln pi4 Lnpi4 pi4mean Lnpi4mean LnDev4

 

. count

29467

 

. sort urut

 

. tempfile kelompok4

 

. save `kelompok4′, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_0500000d.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_0500000d.tmp saved

 

. *3.2.5. Dataset Kelompok 5. Makanan dan Minuman Jadi

. * Terdiri dari 20 komoditi pangan (kode 62-64 dan kode 77-79)

. use $dataproses\data41.dta, clear

 

. order urut kab nks ruta q103 p103 q104 p104 q105 p105 q106 p106 q107 p107 q108 p108 q109 p109 q110 p110 q111 p111 q112 p112 q113 p113 q114 p

> 114 q115 p115 q116 p116 q117 p117 q118 p118 q119 p119 q120 p120 q121 p121 q122 p122 q124 p124 q125 p125 q126 p126

 

. keep urut kab nks ruta q103 p103 q104 p104 q105 p105 q106 p106 q107 p107 q108 p108 q109 p109 q110 p110 q111 p111 q112 p112 q113 p113 q114 p1

> 14 q115 p115 q116 p116 q117 p117 q118 p118 q119 p119 q120 p120 q121 p121 q122 p122 q124 p124 q125 p125 q126 p126

 

. for var q103 q104 q105 q106 q107 q108 q109 q110 q111 q112 q113 q114 q115 ///

>           q116 q117 q118 q119 q120 q121 q122 q124 q125 q126 : replace X=0 if X==.

 

->  replace q103=0 if q103==.

(13425 real changes made)

 

->  replace q104=0 if q104==.

(18247 real changes made)

 

->  replace q105=0 if q105==.

(14835 real changes made)

 

->  replace q106=0 if q106==.

(7107 real changes made)

 

->  replace q107=0 if q107==.

(18250 real changes made)

 

->  replace q108=0 if q108==.

(18681 real changes made)

 

->  replace q109=0 if q109==.

(20912 real changes made)

 

->  replace q110=0 if q110==.

(26089 real changes made)

 

->  replace q111=0 if q111==.

(24486 real changes made)

 

->  replace q112=0 if q112==.

(20642 real changes made)

 

->  replace q113=0 if q113==.

(9652 real changes made)

 

->  replace q114=0 if q114==.

(10955 real changes made)

 

->  replace q115=0 if q115==.

(26240 real changes made)

 

->  replace q116=0 if q116==.

(24973 real changes made)

 

->  replace q117=0 if q117==.

(24805 real changes made)

 

->  replace q118=0 if q118==.

(22298 real changes made)

 

->  replace q119=0 if q119==.

(15170 real changes made)

 

->  replace q120=0 if q120==.

(27904 real changes made)

 

->  replace q121=0 if q121==.

(12471 real changes made)

 

->  replace q122=0 if q122==.

(29404 real changes made)

 

->  replace q124=0 if q124==.

(21300 real changes made)

 

->  replace q125=0 if q125==.

(19380 real changes made)

 

->  replace q126=0 if q126==.

(28735 real changes made)

 

. for var p103 p104 p105 p106 p107 p108 p109 p110 p111 p112 p113 p114 p115 ///

>           p116 p117 p118 p119 p120 p121 p122 p124 p125 p126 : replace X=0 if X==.

 

->  replace p103=0 if p103==.

(13425 real changes made)

 

->  replace p104=0 if p104==.

(18247 real changes made)

 

->  replace p105=0 if p105==.

(14835 real changes made)

 

->  replace p106=0 if p106==.

(7107 real changes made)

 

->  replace p107=0 if p107==.

(18250 real changes made)

 

->  replace p108=0 if p108==.

(18681 real changes made)

 

->  replace p109=0 if p109==.

(20912 real changes made)

 

->  replace p110=0 if p110==.

(26089 real changes made)

 

->  replace p111=0 if p111==.

(24486 real changes made)

 

->  replace p112=0 if p112==.

(20642 real changes made)

 

->  replace p113=0 if p113==.

(9652 real changes made)

 

->  replace p114=0 if p114==.

(10955 real changes made)

 

->  replace p115=0 if p115==.

(26240 real changes made)

 

->  replace p116=0 if p116==.

(24973 real changes made)

 

->  replace p117=0 if p117==.

(24805 real changes made)

 

->  replace p118=0 if p118==.

(22298 real changes made)

 

->  replace p119=0 if p119==.

(15170 real changes made)

 

->  replace p120=0 if p120==.

(27904 real changes made)

 

->  replace p121=0 if p121==.

(12471 real changes made)

 

->  replace p122=0 if p122==.

(29404 real changes made)

 

->  replace p124=0 if p124==.

(21300 real changes made)

 

->  replace p125=0 if p125==.

(19380 real changes made)

 

->  replace p126=0 if p126==.

(28735 real changes made)

 

. *** Konversi satuan Quantity komoditas

. **  potong ke kg , asumsi 1 kg = 8 potong

. gen qkom103=(q103/8)

 

. replace q103=qkom103

(16042 real changes made)

 

. gen qkom106=(q106/8)

 

. replace q106=qkom106

(22360 real changes made)

 

. gen qkom115=(q115/8)

 

. replace q115=qkom115

(3227 real changes made)

 

. gen qkom116=(q116/8)

 

. replace q116=qkom116

(4494 real changes made)

 

. **  ons ke kg , 1 ons = 0.1 kg

. gen qkom104=(q104*0.1)

 

. replace q104=qkom104

(11220 real changes made)

 

. gen qkom114=(q114*0.1)

 

 

 

. replace q114=qkom114

(18512 real changes made)

 

. **  satuan buah kue basah ke kg , asumsi 1 kg = 8 buah

. gen qkom105=(q105/8)

 

. replace q105=qkom105

(14632 real changes made)

 

. **  porsi makanan ke kg , asumsi 1 kg = 4 porsi

. gen qkom107=(q107/4)

 

. replace q107=qkom107

(11217 real changes made)

 

. gen qkom108=(q108/4)

 

. replace q108=qkom108

(10786 real changes made)

 

. gen qkom109=(q109/4)

 

. replace q109=qkom109

(8555 real changes made)

 

. gen qkom110=(q110/4)

 

. replace q110=qkom110

(3378 real changes made)

 

. gen qkom111=(q111/4)

 

. replace q111=qkom111

(4981 real changes made)

 

. gen qkom112=(q112/4)

 

. replace q112=qkom112

(8825 real changes made)

 

. gen qkom113=(q113/4)

 

. replace q113=qkom113

(19815 real changes made)

 

. gen qkom119=(q119/4)

 

. replace q119=qkom119

(14297 real changes made)

 

. **  liter ke kg , asumsi 1 kg = 1 liter

. gen qkom117=(q117*1)

 

. replace q117=qkom117

(0 real changes made)

 

. gen qkom120=(q120*1)

 

. replace q120=qkom120

(0 real changes made)

 

. gen qkom122=(q122*1)

 

. replace q122=qkom122

(0 real changes made)

 

. **  galon ke kg , 1 galon = 5 kg

. gen qkom118=(q118*5)

 

. replace q118=qkom118

(7169 real changes made)

 

. **  gelas ke kg , 4 gelas = 1 kg

. gen qkom121=(q121/4)

 

. replace q121=qkom121

(16996 real changes made)

 

. **  batang ke kg , 1 batang = 0.001 kg

. gen qkom124=(q124*0.001)

 

. replace q124=qkom124

(8167 real changes made)

 

. gen qkom125=(q125*0.001)

 

. replace q125=qkom125

(10087 real changes made)

 

. gen qkom126=(q126*0.001)

 

. replace q126=qkom126

(732 real changes made)

 

. * Hitung unit value, unit value rata-rata dan ln deviasi harga

. gen kuantitas5_minggu=(q103+q104+q105+q106+q107+q108+q109+q110+q111+q112+q113 ///

>                                    +q114+q115+q116+q117+q118+q119+q120+q121+q122+q124+q125+q126)

 

. gen kuantitas5bln = ((kuantitas5_minggu)*(30/7))

 

. gen harga5_minggu=(p103+p104+p105+p106+p107+p108+p109+p110+p111+p112+p113 ///

>                                    +p114+p115+p116+p117+p118+p119+p120+p121+p122+p124+p125+p126)

 

. gen harga5bln = ((harga5_minggu)*(30/7))

 

. gen pi5 = (harga5bln/ kuantitas5bln)

(251 missing values generated)

 

. replace pi5=0 if pi5==.

(251 real changes made)

 

. gen Lnpi5 = ln(1+pi5)

 

. bys kab nks: egen pi5mean=mean(pi5)

 

. gen Lnpi5mean = ln(1+pi5mean)

 

. gen LnDev5 = (Lnpi5-Lnpi5mean)

 

. label variable LnDev5 “Ln deviasi unit value kelompok 5”

 

. label variable kuantitas5_minggu “total kuantitas kelompok 5 seminggu”

 

. label variable kuantitas5bln “total kuantitas kelompok 5 sebulan”

 

. label variable harga5_minggu “total harga kelompok 5 seminggu”

 

. label variable harga5bln “total harga kelompok 5 sebulan”

 

. label variable pi5 “unit value kelompok 5”

 

. label variable Lnpi5 “Ln unit value kelompok 5”

 

. label variable pi5mean “rata-rata unit value kelompok 5”

 

. label variable Lnpi5mean “Ln rata-rata unit value kelompok 5”

 

. keep urut kuantitas5bln harga5bln pi5 Lnpi5 pi5mean Lnpi5mean LnDev5

 

. count

29467

 

. sort urut

 

. tempfile kelompok5

 

. save `kelompok5′, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_0500000e.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_0500000e.tmp saved

 

.

 

 

. *3.2.6. Dataset Kelompok 6. Pangan Lainnya

. * Terdiri dari 19 komoditi pangan (kode 81-86, kode 88-97 dan kode 99-101)

. use $dataproses\data41.dta, clear

 

. order urut kab nks ruta q81 p81 q82 p82 q83 p83 q84 p84 q85 p85 q86 p86 q88 p88 q89 p89 q90 p90 q91 p91 q92 p92 q93 p93 q94 p94 q95 p95 q96

> p96 q97 p97 q99 p99 q100 p100 q101 p101

 

. keep urut kab nks ruta q81 p81 q82 p82 q83 p83 q84 p84 q85 p85 q86 p86 q88 p88 q89 p89 q90 p90 q91 p91 q92 p92 q93 p93 q94 p94 q95 p95 q96 p

> 96 q97 p97 q99 p99 q100 p100 q101 p101

 

. for var q81 q82 q83 q84 q85 q86 q88 q89 q90 q91 q92 q93 q94 q95 q96 q97 q99 ///

>           q100 q101 : replace X=0 if X==.

 

->  replace q81=0 if q81==.

(1339 real changes made)

 

->  replace q82=0 if q82==.

(27159 real changes made)

 

->  replace q83=0 if q83==.

(22988 real changes made)

 

->  replace q84=0 if q84==.

(18277 real changes made)

 

->  replace q85=0 if q85==.

(14443 real changes made)

 

->  replace q86=0 if q86==.

(22053 real changes made)

 

->  replace q88=0 if q88==.

(1343 real changes made)

 

->  replace q89=0 if q89==.

(13789 real changes made)

 

->  replace q90=0 if q90==.

(10416 real changes made)

 

->  replace q91=0 if q91==.

(10809 real changes made)

 

->  replace q92=0 if q92==.

(19837 real changes made)

 

->  replace q93=0 if q93==.

(12289 real changes made)

 

->  replace q94=0 if q94==.

(9711 real changes made)

 

->  replace q95=0 if q95==.

(7670 real changes made)

 

->  replace q96=0 if q96==.

(21755 real changes made)

 

->  replace q97=0 if q97==.

(9341 real changes made)

 

->  replace q99=0 if q99==.

(9048 real changes made)

 

->  replace q100=0 if q100==.

(24919 real changes made)

 

->  replace q101=0 if q101==.

(29150 real changes made)

 

. for var p81 p82 p83 p84 p85 p86 p88 p89 p90 p91 p92 p93 p94 p95 p96 p97 p99 ///

>           p100 p101 : replace X=0 if X==.

 

->  replace p81=0 if p81==.

(1339 real changes made)

 

->  replace p82=0 if p82==.

(27159 real changes made)

 

->  replace p83=0 if p83==.

(22988 real changes made)

 

->  replace p84=0 if p84==.

(18277 real changes made)

 

->  replace p85=0 if p85==.

(14443 real changes made)

 

->  replace p86=0 if p86==.

(22053 real changes made)

 

->  replace p88=0 if p88==.

(1343 real changes made)

 

->  replace p89=0 if p89==.

(13789 real changes made)

 

->  replace p90=0 if p90==.

(10416 real changes made)

 

->  replace p91=0 if p91==.

(10809 real changes made)

 

->  replace p92=0 if p92==.

(19837 real changes made)

 

->  replace p93=0 if p93==.

(12289 real changes made)

 

->  replace p94=0 if p94==.

(9711 real changes made)

 

->  replace p95=0 if p95==.

(7670 real changes made)

 

->  replace p96=0 if p96==.

(21755 real changes made)

 

->  replace p97=0 if p97==.

(9341 real changes made)

 

->  replace p99=0 if p99==.

(9048 real changes made)

 

->  replace p100=0 if p100==.

(24919 real changes made)

 

->  replace p101=0 if p101==.

(29150 real changes made)

 

. *** Konversi satuan Quantity komoditas

. **  ons ke kg , 1 ons = 0.1 kg

. gen qkom81=(q81*0.1)

 

. replace q81=qkom81

(28128 real changes made)

 

. gen qkom82=(q82*0.1)

 

. replace q82=qkom82

(2308 real changes made)

 

. gen qkom83=(q83*0.1)

 

. replace q83=qkom83

(6479 real changes made)

 

. gen qkom85=(q85*0.1)

 

. replace q85=qkom85

(15024 real changes made)

 

. gen qkom100=(q100*0.1)

 

. replace q100=qkom100

(4548 real changes made)

 

. **  2 gram ke kg, 2 gr = 0.002 kg

. gen qkom84=(q84*0.002)

 

. replace q84=qkom84

(11190 real changes made)

 

. **  20 gram ke kg, 20 gr = 0.02 kg

. gen qkom86=(q86*0.02)

 

. replace q86=qkom86

(7414 real changes made)

 

. **  gram ke kg, 1 gr = 0.001 kg

. gen qkom88=(q88*0.001)

 

. replace q88=qkom88

(28124 real changes made)

 

. gen qkom89=(q89*0.001)

 

. replace q89=qkom89

(15678 real changes made)

 

. gen qkom90=(q90*0.001)

 

. replace q90=qkom90

(19051 real changes made)

 

. gen qkom91=(q91*0.001)

 

. replace q91=qkom91

(18658 real changes made)

 

. gen qkom92=(q92*0.001)

 

. replace q92=qkom92

(9630 real changes made)

 

. gen qkom93=(q93*0.001)

 

. replace q93=qkom93

(17178 real changes made)

 

. gen qkom95=(q95*0.001)

 

. replace q95=qkom95

(21797 real changes made)

 

. gen qkom96=(q96*0.001)

 

. replace q96=qkom96

(7712 real changes made)

 

. gen qkom97=(q97*0.001)

 

. replace q97=qkom97

(20126 real changes made)

 

. **  80 gram ke kg, 80 gr = 0.08 kg

. gen qkom99=(q99*0.08)

 

. replace q99=qkom99

(20419 real changes made)

 

. **  150 gram ke kg, 150 gr = 0.15 kg

. gen qkom101=(q101*0.15)

 

. replace q101=qkom101

(317 real changes made)

 

. **  100 ml ke kg, 100 ml = 100/1000liter = 0.1 kg

. gen qkom94=(q94*0.1)

. replace q94=qkom94

(19756 real changes made)

 

. * Hitung unit value, unit value rata-rata dan ln deviasi harga

. gen kuantitas6_minggu=(q81+q82+q83+q84+q85+q86+q88+q89+q90+q91+q92+q93+q94 ///

>                                           +q95+q96+q97+q99+q100+q101)

 

. gen kuantitas6bln = ((kuantitas6_minggu)*(30/7))

 

. gen harga6_minggu=(p81+p82+p83+p84+p85+p86+p88+p89+p90+p91+p92+p93+p94 ///

>                                    +p95+p96+p97+p99+p100+p101)

 

. gen harga6bln = ((harga6_minggu)*(30/7))

 

. gen pi6 = (harga6bln/ kuantitas6bln)

(551 missing values generated)

 

. replace pi6=0 if pi6==.

(551 real changes made)

 

. gen Lnpi6 = ln(1+pi6)

 

. bys kab nks: egen pi6mean=mean(pi6)

 

. gen Lnpi6mean = ln(1+pi6mean)

 

. gen LnDev6 = (Lnpi6-Lnpi6mean)

 

. label variable LnDev6 “Ln deviasi unit value kelompok 6”

 

. label variable kuantitas6_minggu “total kuantitas kelompok 6 seminggu”

 

. label variable kuantitas6bln “total kuantitas kelompok 6 sebulan”

 

. label variable harga6_minggu “total harga kelompok 6 seminggu”

 

. label variable harga6bln “total harga kelompok 6 sebulan”

 

. label variable pi6 “unit value kelompok 6”

 

. label variable Lnpi6 “Ln unit value kelompok 6”

 

. label variable pi6mean “rata-rata unit value kelompok 6”

 

. label variable Lnpi6mean “Ln rata-rata unit value kelompok 6”

 

. keep urut kuantitas6bln harga6bln pi6 Lnpi6 pi6mean Lnpi6mean LnDev6

 

. count

29467

 

. sort urut

 

. tempfile kelompok6

 

. save `kelompok6′, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_0500000f.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_0500000f.tmp saved

 

.

. *3.3. Membuat Variabel Dependen

. **    1. Ln deviasi harga (LnDevi) –> sudah digenerate pada poin 4.2.

. **    2. Peluang konsumsi komoditi pangan (Yprobiti)

. **    3. Budget share komoditi pangan (wi)

.

. *3.3.1. Generate Variabel Ln deviasi harga (Lndevi)

. *       –> sudah digenerate pada poin 5.3.

.

. *3.3.2. Generate Variabel Peluang Konsumsi Kelompok Komoditi (Yprobit_i)

. use $dataproses\kelompok1.dta, clear

 

. *Menyatukan (merging) data dengan menggunakan variabel spesifik

. merge 1:1 urut using `kelompok2′

 

 

 

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. *Menghapus variabel merge

. drop _m

 

. merge 1:1 urut using `kelompok3′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok4′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok5′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok6′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. gen Yprobit1 = 1 if Lnpi1>0

(729 missing values generated)

 

. replace Yprobit1 = 0 if Lnpi1==0

(729 real changes made)

 

. gen Yprobit2 = 1 if Lnpi2>0

(1467 missing values generated)

 

. replace Yprobit2 = 0 if Lnpi2==0

(1467 real changes made)

 

. gen Yprobit3 = 1 if Lnpi3>0

(826 missing values generated)

 

. replace Yprobit3 = 0 if Lnpi3==0

(826 real changes made)

 

. gen Yprobit4 = 1 if Lnpi4>0

(1009 missing values generated)

 

. replace Yprobit4 = 0 if Lnpi4==0

(1009 real changes made)

 

. gen Yprobit5 = 1 if Lnpi5>0

(251 missing values generated)

 

 

 

. replace Yprobit5 = 0 if Lnpi5==0

(251 real changes made)

. gen Yprobit6 = 1 if Lnpi6>0

(551 missing values generated)

 

. replace Yprobit6 = 0 if Lnpi6==0

(551 real changes made)

 

. label variable Yprobit1 “Peluang ruta konsumsi kel 1”

 

. label variable Yprobit2 “Peluang ruta konsumsi kel 2”

 

. label variable Yprobit3 “Peluang ruta konsumsi kel 3”

 

. label variable Yprobit4 “Peluang ruta konsumsi kel 4”

 

. label variable Yprobit5 “Peluang ruta konsumsi kel 5”

 

. label variable Yprobit6 “Peluang ruta konsumsi kel 6”

 

. count

29467

 

. sort urut

 

. tempfile probit_i

 

. save `probit_i’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_0500000g.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_0500000g.tmp saved

 

.

. *3.3.3. Generate Variabel Budget Share Kelompok Komoditi (wi)

. *       –> digenerate pada step 4

.

. **STEP 4. MERGE DATA INDIVIDU, RUTA DAN MODUL

. *         Langkah yang dilakukan :

. *         1. Merge data ruta

. *         2. Merge data individu ke data ruta

. *         3. Merge data modul ke data individu dan data ruta

.

. use $datakor16rt\kor16rt_35.dta, clear

 

. order urut r102 r107 r108 food nfood expend exp_cap

 

. keep urut r102 r107 r108 food nfood expend exp_cap

 

. merge 1:1 urut using `wil’

(label r105 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `milik’

(label r1502 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `miskin’

(label r105 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

. drop _m

 

. merge 1:1 urut using `ipm’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `age’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `jk’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `edu’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `work’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok1′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok2′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok3′

 

 

 

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok4′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok5′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok6′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `probit_i’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. rename r102 kab

 

. rename r107 nks

 

. rename r108 urutruta

 

. count

29467

 

. sort urut

 

. tempfile dataanalisis

 

. save `dataanalisis’, replace

(note: file C:\Users\user\AppData\Local\Temp\ST_0500000h.tmp not found)

file C:\Users\user\AppData\Local\Temp\ST_0500000h.tmp saved

 

. save $dataproses\dataanalisis.dta, replace

file D:\tesis_dewi_aids\running\02_dataproses\\dataanalisis.dta saved

 

. **STEP 5,6,7. ANALISIS DATA

. ***   Analisis data terdiri dari 4 tahap, yaitu

. **    1. Membuat statistik deskripsi

. *                setiap kelompok komoditi. Hal ini dilakukan karena adanya quality

. **    2. Regresi OLS –> untuk mendapatkan Ln deviasi estimasi (LnDeviest) pada

. *                setiap kelompok komoditi. Hal ini dilakukan karena adanya quality

. *                effect dan quantity premium serta adanya simultaneity bias pada

. *                dependen dan independen

 

 

. **    3. Regresi Probit –> untuk mendapatkan Ln deviasi estimasi (LnDeviest) pada

. *                setiap kelompok komoditi. Hal ini dilakukan karena adanya quality

. *                effect dan quantity premium serta adanya simultaneity bias pada variabel

. *                dependen dan independen

. **    4. Regresi SUR –> Meregresikan variabel dependen dan independen dengan

. *                menerapkan restriksi permintaan

.

. *** STEP 5,6,7 RUNNING DATA STATUS IPM=1 (TINGGI/SANGAT TINGGI)

. use $dataproses\dataanalisis.dta, clear

 

. order urut food wil milik work work_i1 work_i2 work_i3 miskin ipm age jk edu kuantitas1bln Lnpi1 LnDev1 kuantitas2bln Lnpi2 LnDev2 kuantitas

> 3bln Lnpi3 LnDev3 kuantitas4bln Lnpi4 LnDev4 kuantitas5bln Lnpi5 LnDev5 kuantitas6bln Lnpi6 LnDev6 Yprobit1 Yprobit2 Yprobit3 Yprobit4 Yprob

> it5 Yprobit6

 

. rename food Y

 

. gen LnY=ln(Y)

 

. gen Lnage=ln(age)

 

. keep if ipm==1

(17552 observations deleted)

 

. * Data continuos

. *Menampilkan statistik deskriptif dari suatu variabel

. sum Y wil milik age jk edu work work_i1 work_i2 work_i3 miskin Lnpi1 Lnpi2 Lnpi3 Lnpi4 Lnpi5 Lnpi6

 

Variable |       Obs        Mean    Std. Dev.       Min        Max

————-+——————————————————–

Y |     11915     1767628     1203934      66630   1.43e+07

wil |     11915    .7645825    .4242773          0          1

milik |     11915    .8512799    .3558273          0          1

age |     11915    51.07772    13.35361         13         97

jk |     11915     .817457    .3863076          0          1

————-+——————————————————–

edu |     11915    .3353756    .4721414          0          1

work |     11915    2.004532    .5976673          1          3

work_i1 |     11915    .1763324    .3811187          0          1

work_i2 |     11915    .6428032    .4791936          0          1

work_i3 |     11915    .1808645     .384922          0          1

————-+——————————————————–

miskin |     11915    .0515317    .2210888          0          1

Lnpi1 |     11915     8.63126    1.740649          0    9.89637

Lnpi2 |     11915    9.555274    2.437058          0   12.66033

Lnpi3 |     11915    8.749838    1.878839          0    10.7052

Lnpi4 |     11915     8.52618    2.070975          0   10.66565

————-+——————————————————–

Lnpi5 |     11915    9.597378     .963689          0   14.15961

Lnpi6 |     11915    9.661745    1.676689          0   13.12236

 

 

. *5 Hasil Analisis Regresi Ln Deviasi Harga

. *5.1. Kelompok 1. Padi dan umbi-umbian

. *Perintah melakukan regresi robust

. reg LnDev1 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   11915

F(  9, 11905) =   39.22

Prob > F      =  0.0000

R-squared     =  0.0751

Root MSE      =  1.6405

 

——————————————————————————

|               Robust

LnDev1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .3366849   .0271949    12.38   0.000     .2833784    .3899914

Lnage |   .7117876   .1042218     6.83   0.000     .5074958    .9160794

jk |   .0372996   .0498199     0.75   0.454    -.0603556    .1349547

edu |  -.0217393   .0364837    -0.60   0.551    -.0932533    .0497748

work_i2 |   .5428255   .0622093     8.73   0.000     .4208851    .6647659

work_i3 |   .5655665   .0584187     9.68   0.000     .4510564    .6800766

miskin |   .3913353   .0441262     8.87   0.000     .3048407      .47783

wil |  -.0277426   .0333224    -0.83   0.405    -.0930599    .0375746

milik |    .735391   .0632763    11.62   0.000     .6113592    .8594228

_cons |  -8.963329   .5893648   -15.21   0.000    -10.11858   -7.808078

——————————————————————————

 

. *Membuat prediksi dari estimasi yang telah dilakukan

. predict LnDev1est

(option xb assumed; fitted values)

 

. *Uji Multikolinieritas

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.07    0.482029

work_i3 |      2.01    0.496761

Lnage |      1.48    0.677610

edu |      1.34    0.747850

LnY |      1.32    0.758008

wil |      1.24    0.806600

jk |      1.20    0.831565

milik |      1.16    0.859064

miskin |      1.11    0.897845

————-+———————-

Mean VIF |      1.44

 

. *Menyimpan hasil regresi ke dalam memori

. eststo dev1_ipm1

 

. *Menampilkan satu atau lebih hasil estimasi dalam suatu tabel secara vertikal

. esttab dev1_ipm1 using $hasil\dev1_ipm1.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev1_ipm1.rtf)

 

. *5.2. Kelompok 2. Ikan, daging, telur dan susu

. reg LnDev2 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   11915

F(  9, 11905) =   69.72

Prob > F      =  0.0000

R-squared     =  0.0867

Root MSE      =  2.2803

 

——————————————————————————

|               Robust

LnDev2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |    .891456    .043185    20.64   0.000     .8068062    .9761057

Lnage |   .3843029   .1304478     2.95   0.003      .128604    .6400019

jk |  -.1131206   .0673804    -1.68   0.093    -.2451972    .0189559

edu |   -.015222   .0484829    -0.31   0.754    -.1102563    .0798123

work_i2 |   .5125815   .0821582     6.24   0.000      .351538     .673625

work_i3 |   .6790828   .0830258     8.18   0.000     .5163387    .8418269

miskin |     .39656   .1050575     3.77   0.000     .1906301    .6024898

wil |  -.1047725   .0495206    -2.12   0.034     -.201841    -.007704

milik |   .7970515   .0784001    10.17   0.000     .6433746    .9507285

_cons |  -15.71247   .7796863   -20.15   0.000    -17.24079   -14.18416

——————————————————————————

 

. predict LnDev2est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.07    0.482029

work_i3 |      2.01    0.496761

Lnage |      1.48    0.677610

edu |      1.34    0.747850

LnY |      1.32    0.758008

wil |      1.24    0.806600

jk |      1.20    0.831565

milik |      1.16    0.859064

miskin |      1.11    0.897845

————-+———————-

Mean VIF |      1.44

 

. eststo dev2_ipm1

. esttab dev2_ipm1 using $hasil\dev2_ipm1.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev2_ipm1.rtf)

 

. *5.3. Kelompok 3. Sayur dan buah-buahan

. reg LnDev3 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   11915

F(  9, 11905) =   46.94

Prob > F      =  0.0000

R-squared     =  0.0763

Root MSE      =  1.7592

 

——————————————————————————

|               Robust

LnDev3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .5576379   .0340448    16.38   0.000     .4909044    .6243713

Lnage |   .5248484   .1064504     4.93   0.000     .3161883    .7335085

jk |  -.1178352   .0515049    -2.29   0.022    -.2187932   -.0168772

edu |   .0129511    .038114     0.34   0.734    -.0617586    .0876608

work_i2 |   .5825874   .0662755     8.79   0.000     .4526765    .7124983

work_i3 |   .6558321    .064955    10.10   0.000     .5285097    .7831545

miskin |    .466657   .0604293     7.72   0.000     .3482056    .5851084

wil |  -.0600666   .0376222    -1.60   0.110    -.1338122    .0136791

milik |   .6072561   .0629173     9.65   0.000      .483928    .7305843

_cons |  -11.23396   .6574513   -17.09   0.000    -12.52267   -9.945247

——————————————————————————

 

. predict LnDev3est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.07    0.482029

work_i3 |      2.01    0.496761

Lnage |      1.48    0.677610

edu |      1.34    0.747850

LnY |      1.32    0.758008

wil |      1.24    0.806600

jk |      1.20    0.831565

milik |      1.16    0.859064

miskin |      1.11    0.897845

————-+———————-

Mean VIF |      1.44

 

. eststo dev3_ipm1

 

. esttab dev3_ipm1 using $hasil\dev3_ipm1.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev3_ipm1.rtf)

 

.

. *5.4. Kelompok 4. Kacang dan minyak

. reg LnDev4 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   11915

F(  9, 11905) =   52.85

Prob > F      =  0.0000

R-squared     =  0.0851

Root MSE      =  1.9364

 

——————————————————————————

|               Robust

LnDev4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .4969555    .034375    14.46   0.000      .429575     .564336

Lnage |   .7981042    .114906     6.95   0.000     .5728697    1.023339

jk |  -.0018398   .0574641    -0.03   0.974    -.1144788    .1107992

edu |  -.0157159   .0426906    -0.37   0.713    -.0993965    .0679647

work_i2 |   .7030953   .0711059     9.89   0.000     .5637161    .8424746

work_i3 |   .7736403   .0691795    11.18   0.000     .6380371    .9092434

miskin |   .5416003   .0600325     9.02   0.000     .4239267    .6592738

wil |  -.0595207   .0400036    -1.49   0.137    -.1379344    .0188929

milik |   .8623373   .0711486    12.12   0.000     .7228745      1.0018

_cons |  -11.90925   .6792073   -17.53   0.000    -13.24061    -10.5779

——————————————————————————

. predict LnDev4est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.07    0.482029

work_i3 |      2.01    0.496761

Lnage |      1.48    0.677610

edu |      1.34    0.747850

LnY |      1.32    0.758008

wil |      1.24    0.806600

jk |      1.20    0.831565

milik |      1.16    0.859064

miskin |      1.11    0.897845

————-+———————-

Mean VIF |      1.44

 

. eststo dev4_ipm1

 

. esttab dev4_ipm1 using $hasil\dev4_ipm1.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev4_ipm1.rtf)

 

.

. *5.5. Kelompok 5. Makanan jadi dan rokok

. reg LnDev5 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   11915

F(  9, 11905) =   44.91

Prob > F      =  0.0000

R-squared     =  0.0895

Root MSE      =   .8956

 

——————————————————————————

|               Robust

LnDev5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .3808318    .022517    16.91   0.000     .3366949    .4249687

Lnage |  -.0804113   .0341122    -2.36   0.018    -.1472768   -.0135458

jk |   .1021809   .0283417     3.61   0.000     .0466264    .1577353

edu |  -.1373052   .0180406    -7.61   0.000    -.1726677   -.1019428

work_i2 |  -.0704911   .0267929    -2.63   0.009    -.1230095   -.0179727

work_i3 |  -.0509972    .037595    -1.36   0.175    -.1246894    .0226951

miskin |  -.2900429   .0704717    -4.12   0.000    -.4281789    -.151907

wil |   .0482989   .0239278     2.02   0.044     .0013965    .0952013

milik |  -.0024865   .0241049    -0.10   0.918     -.049736    .0447629

_cons |  -5.300325   .2771076   -19.13   0.000    -5.843501   -4.757149

——————————————————————————

 

. predict LnDev5est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.07    0.482029

work_i3 |      2.01    0.496761

Lnage |      1.48    0.677610

edu |      1.34    0.747850

LnY |      1.32    0.758008

wil |      1.24    0.806600

jk |      1.20    0.831565

milik |      1.16    0.859064

miskin |      1.11    0.897845

————-+———————-

Mean VIF |      1.44

 

. eststo dev5_ipm1

 

. esttab dev5_ipm1 using $hasil\dev5_ipm1.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev5_ipm1.rtf)

 

.

 

 

. *5.6. Kelompok 6. Bahan Pangan Lainnya

. reg LnDev6 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   11915

F(  9, 11905) =   33.91

Prob > F      =  0.0000

R-squared     =  0.0700

Root MSE      =  1.5868

 

——————————————————————————

|               Robust

LnDev6 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .3680363   .0280737    13.11   0.000     .3130072    .4230654

Lnage |   .6256827   .1078128     5.80   0.000     .4143519    .8370134

jk |  -.0105195   .0487762    -0.22   0.829    -.1061287    .0850898

edu |   .0033167   .0362143     0.09   0.927    -.0676693    .0743027

work_i2 |   .5562008   .0635811     8.75   0.000     .4315714    .6808302

work_i3 |   .5640973   .0604696     9.33   0.000      .445567    .6826275

miskin |    .292219   .0491996     5.94   0.000     .1957796    .3886583

wil |  -.0436218   .0313264    -1.39   0.164    -.1050267     .017783

milik |   .6154437   .0611882    10.06   0.000     .4955049    .7353825

_cons |  -8.893531   .6226617   -14.28   0.000    -10.11405   -7.673012

——————————————————————————

 

. predict LnDev6est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.07    0.482029

work_i3 |      2.01    0.496761

Lnage |      1.48    0.677610

edu |      1.34    0.747850

LnY |      1.32    0.758008

wil |      1.24    0.806600

jk |      1.20    0.831565

milik |      1.16    0.859064

miskin |      1.11    0.897845

————-+———————-

Mean VIF |      1.44

 

. eststo dev6_ipm1

 

. esttab dev6_ipm1 using $hasil\dev6_ipm1.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev6_ipm1.rtf)

 

.

. *5.1.1. Generate Lnpiest

. gen Lnpi1est=(Lnpi1-LnDev1est) if Yprobit1==1

(458 missing values generated)

 

. replace Lnpi1est=(Lnpi1mean- LnDev1est) if Yprobit1==0

(458 real changes made)

 

. gen Lnpi2est=(Lnpi2-LnDev2est) if Yprobit2==1

(707 missing values generated)

 

. replace Lnpi2est=(Lnpi2mean- LnDev2est) if Yprobit2==0

(707 real changes made)

 

. gen Lnpi3est=(Lnpi3-LnDev3est) if Yprobit3==1

(500 missing values generated)

 

. replace Lnpi3est=(Lnpi3mean- LnDev3est) if Yprobit3==0

(500 real changes made)

 

. gen Lnpi4est=(Lnpi4-LnDev4est) if Yprobit4==1

(653 missing values generated)

 

. replace Lnpi4est=(Lnpi4mean- LnDev4est) if Yprobit4==0

(653 real changes made)

 

. gen Lnpi5est=(Lnpi5-LnDev5est) if Yprobit5==1

(61 missing values generated)

. replace Lnpi5est=(Lnpi5mean- LnDev5est) if Yprobit5==0

(61 real changes made)

 

. gen Lnpi6est=(Lnpi6-LnDev6est) if Yprobit6==1

(336 missing values generated)

 

. replace Lnpi6est=(Lnpi6mean- LnDev6est) if Yprobit6==0

(336 real changes made)

 

. label variable Lnpi1est “Ln unit value kel 1 hasil estimasi”

 

. label variable Lnpi2est “Ln unit value kel 2 hasil estimasi”

 

. label variable Lnpi3est “Ln unit value kel 3 hasil estimasi”

 

. label variable Lnpi4est “Ln unit value kel 4 hasil estimasi”

 

. label variable Lnpi5est “Ln unit value kel 5 hasil estimasi”

 

. label variable Lnpi6est “Ln unit value kel 6 hasil estimasi”

 

. sort urut

 

. *5.1.2. Generate Variabel Budget Share kelompok komoditi ke-i(wi)

. gen w1 = (harga1bln/Y)

 

. gen w2 = (harga2bln/Y)

 

. gen w3 = (harga3bln/Y)

 

. gen w4 = (harga4bln/Y)

 

. gen w5 = (harga5bln/Y)

 

. gen w6 = (harga6bln/Y)

 

. label variable w1 “budget share kel 1”

 

. label variable w2 “budget share kel 2”

 

. label variable w3 “budget share kel 3”

 

. label variable w4 “budget share kel 4”

 

. label variable w5 “budget share kel 5”

 

. label variable w6 “budget share kel 6”

 

. count

11915

 

. sort urut

 

. *6 Analisis Regresi Probit

. *6.1. Kelompok 1. Padi dan umbi-umbian

. *Melakukan regresi probit

. probit Yprobit1 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -1941.5598

Iteration 1:   log likelihood =  -1427.619

Iteration 2:   log likelihood = -1378.0283

Iteration 3:   log likelihood = -1377.1792

Iteration 4:   log likelihood = -1377.1787

Iteration 5:   log likelihood = -1377.1787

 

Probit regression                                 Number of obs   =      11915

LR chi2(15)     =    1128.76

Prob > chi2     =     0.0000

Log likelihood = -1377.1787                       Pseudo R2       =     0.2907

 

 

 

——————————————————————————

Yprobit1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   2.157721   .1367046    15.78   0.000     1.889785    2.425657

Lnpi2est |  -.2020716   .0717782    -2.82   0.005    -.3427543   -.0613888

Lnpi3est |  -.2594836   .0714312    -3.63   0.000    -.3994861    -.119481

Lnpi4est |   .7882882   .1093781     7.21   0.000     .5739112    1.002665

Lnpi5est |  -.2621549   .0367569    -7.13   0.000    -.3341971   -.1901128

Lnpi6est |  -.8917982   .0881925   -10.11   0.000    -1.064652   -.7189442

LnY |   .7008863   .0656713    10.67   0.000      .572173    .8295996

Lnage |   1.843502   .1360399    13.55   0.000     1.576869    2.110136

jk |   .2529961   .0671783     3.77   0.000      .121329    .3846633

edu |  -.2275222   .0636262    -3.58   0.000    -.3522273   -.1028172

work_i2 |    1.49648   .1082974    13.82   0.000     1.284221    1.708739

work_i3 |   2.042483   .1516368    13.47   0.000      1.74528    2.339686

miskin |    2.08915   .2235579     9.35   0.000     1.650985    2.527316

wil |   .0755553   .0739458     1.02   0.307    -.0693759    .2204865

milik |   2.088714   .1278972    16.33   0.000      1.83804    2.339388

_cons |  -29.44048   2.537195   -11.60   0.000    -34.41329   -24.46767

——————————————————————————

. *Menampilkan hasil regresi, membuat statistik deskriptif dan dasar crosstab

. outreg2 using $hasil\probit1_ipm1,dec(3) replace

dir : seeout

 

. predict probitx1, xb

 

. *Menampilkan marginal effect regresi probit

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      11915

Model VCE    : OIM

 

Expression   : Pr(Yprobit1), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .1313291   .0087765    14.96   0.000     .1141275    .1485307

Lnpi2est |   -.012299   .0043782    -2.81   0.005    -.0208802   -.0037179

Lnpi3est |  -.0157934   .0043604    -3.62   0.000    -.0243397   -.0072471

Lnpi4est |    .047979   .0067719     7.08   0.000     .0347062    .0612517

Lnpi5est |   -.015956    .002262    -7.05   0.000    -.0203895   -.0115225

Lnpi6est |  -.0542791   .0055074    -9.86   0.000    -.0650733   -.0434848

LnY |   .0426593    .004131    10.33   0.000     .0345626     .050756

Lnage |   .1122043   .0086739    12.94   0.000     .0952038    .1292047

jk |   .0153985   .0041023     3.75   0.000     .0073582    .0234389

edu |  -.0138481   .0038882    -3.56   0.000    -.0214689   -.0062273

work_i2 |   .0910829   .0069079    13.19   0.000     .0775436    .1046221

work_i3 |   .1243152    .009717    12.79   0.000     .1052702    .1433602

miskin |   .1271556    .013957     9.11   0.000     .0998004    .1545107

wil |   .0045987   .0045028     1.02   0.307    -.0042267     .013424

milik |    .127129   .0083312    15.26   0.000     .1108002    .1434579

——————————————————————————

 

. outreg2 using $hasil\probit1_ipm1mfx.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit1_ipm1mfx.xls

dir : seeout

 

. *6.2. Kelompok 2. Ikan, daging, telur dan susu

. probit Yprobit2 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -2682.5343

Iteration 1:   log likelihood = -2200.1672

Iteration 2:   log likelihood = -2166.7712

Iteration 3:   log likelihood = -2166.6576

Iteration 4:   log likelihood = -2166.6576

 

Probit regression                                 Number of obs   =      11915

LR chi2(15)     =    1031.75

Prob > chi2     =     0.0000

Log likelihood = -2166.6576                       Pseudo R2       =     0.1923

——————————————————————————

Yprobit2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |    .382445   .1116183     3.43   0.001     .1636771    .6012128

Lnpi2est |   .0918301   .0625349     1.47   0.142    -.0307361    .2143963

Lnpi3est |  -.2832031   .0564146    -5.02   0.000    -.3937737   -.1726325

Lnpi4est |   .9753047   .0894035    10.91   0.000     .8000771    1.150532

Lnpi5est |  -.2807996    .027662   -10.15   0.000     -.335016   -.2265831

Lnpi6est |  -.4756134   .0736702    -6.46   0.000    -.6200043   -.3312224

LnY |   .9188036    .056061    16.39   0.000     .8089261    1.028681

Lnage |   .7615852   .1094254     6.96   0.000     .5471153    .9760551

jk |  -.0228207   .0550445    -0.41   0.678    -.1307059    .0850646

edu |  -.0950348    .053355    -1.78   0.075    -.1996087    .0095392

work_i2 |   .8577559   .0859197     9.98   0.000     .6893564    1.026155

work_i3 |     1.2286   .1097644    11.19   0.000     1.013466    1.443734

miskin |   .8830632   .1102208     8.01   0.000     .6670343    1.099092

wil |  -.0269949   .0578441    -0.47   0.641    -.1403674    .0863775

milik |   1.242165   .1027378    12.09   0.000     1.040803    1.443527

_cons |   -19.3908   2.064822    -9.39   0.000    -23.43777   -15.34382

——————————————————————————

 

. outreg2 using $hasil\probit2_ipm1,dec(3) replace

dir : seeout

 

. predict probitx2, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      11915

Model VCE    : OIM

 

Expression   : Pr(Yprobit2), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0369487   .0108071     3.42   0.001     .0157671    .0581303

Lnpi2est |   .0088719   .0060416     1.47   0.142    -.0029695    .0207132

Lnpi3est |  -.0273608   .0054658    -5.01   0.000    -.0380735   -.0166481

Lnpi4est |    .094226   .0087559    10.76   0.000     .0770647    .1113874

Lnpi5est |  -.0271286   .0026811   -10.12   0.000    -.0323834   -.0218738

Lnpi6est |  -.0459499   .0071562    -6.42   0.000    -.0599757   -.0319241

LnY |   .0887673   .0056005    15.85   0.000     .0777905    .0997442

Lnage |   .0735782   .0106455     6.91   0.000     .0527134     .094443

jk |  -.0022047   .0053181    -0.41   0.678     -.012628    .0082185

edu |  -.0091815   .0051576    -1.78   0.075    -.0192902    .0009272

work_i2 |   .0828694   .0083951     9.87   0.000     .0664153    .0993235

work_i3 |   .1186974   .0107731    11.02   0.000     .0975824    .1398124

miskin |   .0853144   .0107394     7.94   0.000     .0642656    .1063632

wil |   -.002608   .0055883    -0.47   0.641    -.0135609    .0083448

milik |   .1200079   .0100897    11.89   0.000     .1002325    .1397833

——————————————————————————

 

. outreg2 using $hasil\probit2_ipm1mfx.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit2_ipm1mfx.xls

dir : seeout

 

. *6.3. Kelompok 3. Sayur dan buah-buahan

. probit Yprobit3 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -2074.8318

Iteration 1:   log likelihood = -1691.5157

Iteration 2:   log likelihood = -1661.9095

Iteration 3:   log likelihood =  -1661.716

Iteration 4:   log likelihood = -1661.7159

 

Probit regression                                 Number of obs   =      11915

LR chi2(15)     =     826.23

Prob > chi2     =     0.0000

Log likelihood = -1661.7159                       Pseudo R2       =     0.1991

 

 

 

——————————————————————————

Yprobit3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .3979719   .1234249     3.22   0.001     .1560635    .6398802

Lnpi2est |  -.1298736   .0655649    -1.98   0.048    -.2583784   -.0013688

Lnpi3est |   .2011805   .0670663     3.00   0.003     .0697329     .332628

Lnpi4est |    .641353   .0970839     6.61   0.000      .451072     .831634

Lnpi5est |   -.235509   .0322205    -7.31   0.000      -.29866   -.1723581

Lnpi6est |  -.4650127    .079954    -5.82   0.000    -.6217195   -.3083058

LnY |   .8029653    .060485    13.28   0.000     .6844169    .9215138

Lnage |   .9297938   .1195497     7.78   0.000     .6954807    1.164107

jk |  -.1120656    .062491    -1.79   0.073    -.2345457    .0104146

edu |  -.0547999   .0590009    -0.93   0.353    -.1704396    .0608397

work_i2 |   .9589336    .094823    10.11   0.000     .7730839    1.144783

work_i3 |   1.366379   .1269314    10.76   0.000     1.117598     1.61516

miskin |    1.42301   .1588676     8.96   0.000     1.111635    1.734385

wil |  -.0164035   .0660987    -0.25   0.804    -.1459545    .1131474

milik |    1.10778   .1128959     9.81   0.000     .8865086    1.329052

_cons |  -17.93128   2.264041    -7.92   0.000    -22.36872   -13.49384

——————————————————————————

 

. outreg2 using $hasil\probit3_ipm3,dec(3) replace

dir : seeout

 

. predict probitx3, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      11915

Model VCE    : OIM

 

Expression   : Pr(Yprobit3), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0291883   .0090792     3.21   0.001     .0113933    .0469833

Lnpi2est |  -.0095253   .0048159    -1.98   0.048    -.0189643   -.0000862

Lnpi3est |   .0147551   .0049319     2.99   0.003     .0050888    .0244214

Lnpi4est |   .0470384   .0072097     6.52   0.000     .0329078    .0611691

Lnpi5est |  -.0172728    .002385    -7.24   0.000    -.0219474   -.0125983

Lnpi6est |  -.0341052   .0059123    -5.77   0.000    -.0456932   -.0225172

LnY |   .0588915   .0046643    12.63   0.000     .0497496    .0680334

Lnage |   .0681934    .008914     7.65   0.000     .0507223    .0856645

jk |  -.0082192   .0045883    -1.79   0.073     -.017212    .0007737

edu |  -.0040192   .0043285    -0.93   0.353    -.0125029    .0044645

work_i2 |   .0703306   .0071395     9.85   0.000     .0563375    .0843237

work_i3 |   .1002137   .0096308    10.41   0.000     .0813376    .1190897

miskin |   .1043671   .0119214     8.75   0.000     .0810016    .1277326

wil |  -.0012031   .0048478    -0.25   0.804    -.0107045    .0082984

milik |   .0812474   .0085025     9.56   0.000     .0645829    .0979119

——————————————————————————

 

. outreg2 using $hasil\probit3_ipm3mfx.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit3_ipm3mfx.xls

dir : seeout

 

. *6.4. Kelompok 4. Kacang dan minyak

. probit Yprobit4 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -2531.0666

Iteration 1:   log likelihood = -1938.9338

Iteration 2:   log likelihood = -1894.6437

Iteration 3:   log likelihood = -1894.2194

Iteration 4:   log likelihood = -1894.2189

Iteration 5:   log likelihood = -1894.2189

 

Probit regression                                 Number of obs   =      11915

LR chi2(15)     =    1273.70

Prob > chi2     =     0.0000

Log likelihood = -1894.2189                       Pseudo R2       =     0.2516

 

——————————————————————————

Yprobit4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .6165405   .1184238     5.21   0.000     .3844342    .8486468

Lnpi2est |  -.3233006   .0600088    -5.39   0.000    -.4409157   -.2056855

Lnpi3est |  -.2628054    .061275    -4.29   0.000    -.3829023   -.1427086

Lnpi4est |   1.600551   .0960237    16.67   0.000     1.412348    1.788754

Lnpi5est |  -.2727445   .0308452    -8.84   0.000       -.3332    -.212289

Lnpi6est |  -.7205244   .0767116    -9.39   0.000    -.8708764   -.5701724

LnY |   .7030366    .055983    12.56   0.000     .5933119    .8127613

Lnage |   1.386073    .118877    11.66   0.000     1.153078    1.619067

jk |   .1020825    .058628     1.74   0.082    -.0128262    .2169912

edu |  -.1045355   .0552182    -1.89   0.058    -.2127611    .0036901

work_i2 |   1.290894   .0939453    13.74   0.000     1.106765    1.475023

work_i3 |   1.753879   .1271219    13.80   0.000     1.504725    2.003034

miskin |   1.683411   .1620573    10.39   0.000     1.365785    2.001038

wil |   .0546855   .0640987     0.85   0.394    -.0709456    .1803167

milik |    1.61865   .1100867    14.70   0.000     1.402884    1.834416

_cons |  -20.92857   2.184671    -9.58   0.000    -25.21045    -16.6467

——————————————————————————

 

. outreg2 using $hasil\probit4_ipm4,dec(3) replace

dir : seeout

 

. predict probitx4, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      11915

Model VCE    : OIM

 

Expression   : Pr(Yprobit4), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0524192    .010116     5.18   0.000     .0325921    .0722463

Lnpi2est |  -.0274875   .0051209    -5.37   0.000    -.0375243   -.0174507

Lnpi3est |  -.0223441   .0052211    -4.28   0.000    -.0325772    -.012111

Lnpi4est |   .1360813   .0084304    16.14   0.000     .1195581    .1526045

Lnpi5est |  -.0231892   .0026348    -8.80   0.000    -.0283533    -.018025

Lnpi6est |  -.0612601   .0065945    -9.29   0.000    -.0741851    -.048335

LnY |   .0597732   .0048672    12.28   0.000     .0502338    .0693127

Lnage |    .117846   .0102903    11.45   0.000     .0976775    .1380146

jk |   .0086792   .0049875     1.74   0.082    -.0010961    .0184545

edu |  -.0088878    .004699    -1.89   0.059    -.0180977    .0003222

work_i2 |   .1097538    .008177    13.42   0.000     .0937271    .1257804

work_i3 |   .1491175     .01113    13.40   0.000      .127303    .1709319

miskin |   .1431262   .0139942    10.23   0.000     .1156981    .1705542

wil |   .0046494   .0054507     0.85   0.394    -.0060338    .0153327

milik |   .1376201   .0096267    14.30   0.000     .1187522     .156488

——————————————————————————

 

. outreg2 using $hasil\probit4_ipm4mfx.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit4_ipm4mfx.xls

dir : seeout

 

. *6.5. Kelompok 5. Makanan jadi dan rokok

. probit Yprobit5 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -382.59904

Iteration 1:   log likelihood = -264.21598

Iteration 2:   log likelihood = -229.12944

Iteration 3:   log likelihood = -226.96879

Iteration 4:   log likelihood = -226.96306

Iteration 5:   log likelihood = -226.96306

 

Probit regression                                 Number of obs   =      11915

LR chi2(15)     =     311.27

Prob > chi2     =     0.0000

Log likelihood = -226.96306                       Pseudo R2       =     0.4068

 

 

 

——————————————————————————

Yprobit5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |  -.2699264   .3472055    -0.78   0.437    -.9504367    .4105839

Lnpi2est |  -.1852032   .1615033    -1.15   0.251    -.5017439    .1313374

Lnpi3est |   .2820793   .1582228     1.78   0.075    -.0280318    .5921903

Lnpi4est |  -.3465198   .2691026    -1.29   0.198    -.8739512    .1809117

Lnpi5est |  -.3789836   .0566275    -6.69   0.000    -.4899716   -.2679957

Lnpi6est |  -.1668603    .216739    -0.77   0.441     -.591661    .2579403

LnY |   .8772157   .1760559     4.98   0.000     .5321526    1.222279

Lnage |  -1.196113   .4086769    -2.93   0.003    -1.997106   -.3951214

jk |  -.1360959   .1550544    -0.88   0.380     -.439997    .1678052

edu |  -.3554285   .2018377    -1.76   0.078    -.7510231     .040166

work_i2 |  -.7767989   .2851502    -2.72   0.006    -1.335683   -.2179147

work_i3 |  -.9199008   .3101152    -2.97   0.003    -1.527715   -.3120862

miskin |  -.2456714   .2533334    -0.97   0.332    -.7421958    .2508529

wil |   .2596464   .1445319     1.80   0.072     -.023631    .5429237

milik |  -.5363877     .35501    -1.51   0.131    -1.232194    .1594191

_cons |   7.458328   6.740445     1.11   0.269    -5.752701    20.66936

——————————————————————————

Note: 0 failures and 6 successes completely determined.

 

. outreg2 using $hasil\probit5_ipm5,dec(3) replace

dir : seeout

 

. predict probitx5, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      11915

Model VCE    : OIM

 

Expression   : Pr(Yprobit5), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |  -.0026054   .0033602    -0.78   0.438    -.0091912    .0039804

Lnpi2est |  -.0017877   .0015658    -1.14   0.254    -.0048566    .0012813

Lnpi3est |   .0027227   .0015508     1.76   0.079    -.0003168    .0057623

Lnpi4est |  -.0033447   .0026124    -1.28   0.200    -.0084649    .0017754

Lnpi5est |  -.0036581   .0006324    -5.78   0.000    -.0048977   -.0024185

Lnpi6est |  -.0016106   .0020975    -0.77   0.443    -.0057215    .0025003

LnY |   .0084672   .0018681     4.53   0.000     .0048059    .0121286

Lnage |  -.0115454   .0040775    -2.83   0.005    -.0195371   -.0035536

jk |  -.0013137   .0015009    -0.88   0.381    -.0042554    .0016281

edu |  -.0034307   .0019733    -1.74   0.082    -.0072983    .0004368

work_i2 |   -.007498   .0028226    -2.66   0.008    -.0130302   -.0019657

work_i3 |  -.0088793   .0030854    -2.88   0.004    -.0149266   -.0028319

miskin |  -.0023713   .0024486    -0.97   0.333    -.0071705    .0024279

wil |   .0025062   .0014122     1.77   0.076    -.0002617    .0052741

milik |  -.0051774   .0034543    -1.50   0.134    -.0119478    .0015929

——————————————————————————

 

. outreg2 using $hasil\probit5_ipm5mfx.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit5_ipm5mfx.xls

dir : seeout

 

. *6.6. Kelompok 6. Bahan Pangan Lainnya

. probit Yprobit6 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -1530.2139

Iteration 1:   log likelihood = -1200.6418

Iteration 2:   log likelihood = -1167.7384

Iteration 3:   log likelihood = -1167.3658

Iteration 4:   log likelihood = -1167.3657

 

Probit regression                                 Number of obs   =      11915

LR chi2(15)     =     725.70

Prob > chi2     =     0.0000

Log likelihood = -1167.3657                       Pseudo R2       =     0.2371

 

 

 

——————————————————————————

Yprobit6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .5303744   .1442635     3.68   0.000     .2476232    .8131256

Lnpi2est |  -.2878453   .0732063    -3.93   0.000    -.4313271   -.1443636

Lnpi3est |  -.1974047   .0760001    -2.60   0.009    -.3463622   -.0484472

Lnpi4est |   .9119413   .1157797     7.88   0.000     .6850172    1.138865

Lnpi5est |  -.2086059   .0381503    -5.47   0.000    -.2833791   -.1338328

Lnpi6est |    .310206    .106115     2.92   0.003     .1022244    .5181877

LnY |   .7019068   .0701326    10.01   0.000     .5644495    .8393642

Lnage |   1.585403   .1415088    11.20   0.000     1.308051    1.862755

jk |   .0997019   .0709324     1.41   0.160     -.039323    .2387269

edu |  -.0946232   .0683262    -1.38   0.166    -.2285401    .0392936

work_i2 |   1.420909    .114205    12.44   0.000     1.197071    1.644747

work_i3 |   1.837005   .1602073    11.47   0.000     1.523004    2.151005

miskin |   1.573445    .194906     8.07   0.000     1.191436    1.955454

wil |   .0624309   .0803878     0.78   0.437    -.0951263     .219988

milik |   1.706133   .1336687    12.76   0.000     1.444147    1.968119

_cons |  -26.40024   2.716553    -9.72   0.000    -31.72458   -21.07589

——————————————————————————

 

. outreg2 using $hasil\probit6_ipm6,dec(3) replace

dir : seeout

 

. predict probitx6, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      11915

Model VCE    : OIM

 

Expression   : Pr(Yprobit6), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0269204   .0073695     3.65   0.000     .0124764    .0413643

Lnpi2est |  -.0146102   .0037424    -3.90   0.000    -.0219452   -.0072753

Lnpi3est |  -.0100197   .0038664    -2.59   0.010    -.0175977   -.0024417

Lnpi4est |   .0462877   .0060538     7.65   0.000     .0344223     .058153

Lnpi5est |  -.0105883   .0019606    -5.40   0.000     -.014431   -.0067455

Lnpi6est |   .0157452   .0054164     2.91   0.004     .0051293    .0263611

LnY |   .0356269   .0037584     9.48   0.000     .0282606    .0429931

Lnage |   .0804708   .0076266    10.55   0.000     .0655229    .0954186

jk |   .0050606    .003604     1.40   0.160     -.002003    .0121242

edu |  -.0048028   .0034718    -1.38   0.167    -.0116075    .0020018

work_i2 |   .0721215   .0062346    11.57   0.000     .0599019    .0843411

work_i3 |   .0932414   .0087157    10.70   0.000     .0761589    .1103239

miskin |   .0798638   .0102319     7.81   0.000     .0598097    .0999179

wil |   .0031688   .0040817     0.78   0.438    -.0048311    .0111687

milik |   .0865987   .0073636    11.76   0.000     .0721663    .1010311

——————————————————————————

 

. outreg2 using $hasil\probit6_ipm6mfx.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit6_ipm6mfx.xls

dir : seeout

 

.

. *6.1.1. Generate Invers Mills RAtio (IMR)

. gen IMR1 = (normalden( probitx1)/normprob( probitx1))

 

. gen IMR2 = (normalden( probitx2)/normprob( probitx2))

 

. gen IMR3 = (normalden( probitx3)/normprob( probitx3))

 

. gen IMR4 = (normalden( probitx4)/normprob( probitx4))

 

. gen IMR5 = (normalden( probitx5)/normprob( probitx5))

 

. gen IMR6 = (normalden( probitx6)/normprob( probitx6))

 

. label variable IMR1 “Inverse Mills Ratio Kel 1”

 

. label variable IMR2 “Inverse Mills Ratio Kel 2”

 

. label variable IMR3 “Inverse Mills Ratio Kel 3”

 

. label variable IMR4 “Inverse Mills Ratio Kel 4”

 

. label variable IMR5 “Inverse Mills Ratio Kel 5”

 

. label variable IMR6 “Inverse Mills Ratio Kel 6”

 

.

. *6.1.2. Generate Indeks Harga Stone

. gen LnIndexStone=((w1*Lnpi1est)+(w2*Lnpi2est)+(w3*Lnpi3est)+(w4*Lnpi4est)+(w5*Lnpi5est)+(w6*Lnpi6est))

 

. gen LnY_riil = (LnY- LnIndexStone)

 

. label variable LnIndexStone “Ln Indeks harga Stone”

 

. label variable LnY_riil “Ln Pengeluaran riil”

 

. *7. Analisis Regresi Dengan Menerapkan Restriksi Permintaan (LA_AIDS Model)

. **  Menerapkan Restriksi Permintaan, yang terdiri atas :

. *         1. Simetri

. *         2. Adding Up

. *         3. Homogeneity

 

. constraint define 1 [w1]Lnpi2est=[w2]Lnpi1est

 

. constraint define 2 [w1]Lnpi3est=[w3]Lnpi1est

 

. constraint define 3 [w1]Lnpi4est=[w4]Lnpi1est

 

. constraint define 4 [w1]Lnpi5est=[w5]Lnpi1est

 

. constraint define 5 [w1]Lnpi6est=[w6]Lnpi1est

 

. constraint define 6 [w2]Lnpi3est=[w3]Lnpi2est

 

. constraint define 7 [w2]Lnpi4est=[w4]Lnpi2est

 

. constraint define 8 [w2]Lnpi5est=[w5]Lnpi2est

 

. constraint define 9 [w2]Lnpi6est=[w6]Lnpi2est

 

. constraint define 10 [w3]Lnpi4est=[w4]Lnpi3est

 

. constraint define 11 [w3]Lnpi5est=[w5]Lnpi3est

 

. constraint define 12 [w3]Lnpi6est=[w6]Lnpi3est

 

. constraint define 13 [w4]Lnpi5est=[w5]Lnpi4est

 

. constraint define 14 [w4]Lnpi6est=[w6]Lnpi4est

 

. constraint define 15 [w5]Lnpi6est=[w6]Lnpi5est

 

. constraint define 16 [w1]Lnpi1est=-[w1]Lnpi2est-[w1]Lnpi3est-[w1]Lnpi4est-[w1]Lnpi5est-[w1]Lnpi6est

 

. constraint define 17 [w2]Lnpi2est=-[w2]Lnpi1est-[w2]Lnpi3est-[w2]Lnpi4est-[w2]Lnpi5est-[w2]Lnpi6est

 

. constraint define 18 [w3]Lnpi3est=-[w3]Lnpi1est-[w3]Lnpi2est-[w3]Lnpi4est-[w3]Lnpi5est-[w3]Lnpi6est

 

. constraint define 19 [w4]Lnpi4est=-[w4]Lnpi1est-[w4]Lnpi2est-[w4]Lnpi3est-[w4]Lnpi5est-[w4]Lnpi6est

 

. constraint define 20 [w5]Lnpi5est=-[w5]Lnpi1est-[w5]Lnpi2est-[w5]Lnpi3est-[w5]Lnpi4est-[w5]Lnpi6est

 

. constraint define 21 [w6]Lnpi6est=-[w6]Lnpi1est-[w6]Lnpi2est-[w6]Lnpi3est-[w6]Lnpi4est-[w6]Lnpi5est

 

. constraint 22 [w1]LnY_riil=-[w2]LnY_riil-[w3]LnY_riil-[w4]LnY_riil-[w5]LnY_riil-[w6]LnY_riil

 

. constraint 23 [w1]_cons+[w2]_cons+[w3]_cons+[w4]_cons+[w5]_cons+[w6]_cons=1

 

. *  melakukan setting terhadap jumlah variabel maksimum yang dapat disertakan dalam perintah estimasi stata

. set matsize 800

 

. *  running regresi sur

. reg3 (w1 = Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est ///

> LnY_riil Lnage jk edu work_i2 work_i3 miskin wil milik IMR1) (w2=Lnpi1est ///

> Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk edu work_i2 ///

> work_i3 miskin wil milik IMR2) (w3=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est ///

> Lnpi6est LnY_riil Lnage jk edu work_i2 work_i3 miskin wil milik IMR3) ///

> (w4=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk ///

> edu work_i2 work_i3 miskin wil milik IMR4) (w5=Lnpi1est Lnpi2est Lnpi3est ///

> Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk edu work_i2 work_i3 miskin wil ///

> milik IMR5) (w6=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil ///

> Lnage jk edu work_i2 work_i3 miskin wil milik IMR6) , constraints(23 22 21 20 ///

> 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1) ireg3 nodfk sure corr(independent)

 

Iteration 1:   tolerance =  .07434211

Iteration 2:   tolerance =  .00157933

Iteration 3:   tolerance =  .00006729

Iteration 4:   tolerance =  2.115e-06

Iteration 5:   tolerance =  6.135e-08

 

Seemingly unrelated regression, iterated

———————————————————————-

Equation          Obs  Parms        RMSE    “R-sq”       chi2        P

———————————————————————-

w1              11915     15    .0618442    0.3110    5566.68   0.0000

w2              11915     15    .0902789    0.2492    4369.63   0.0000

w3              11915     15    .0627554    0.1005    1431.48   0.0000

w4              11915     15    .0410887    0.2475    4058.72   0.0000

w5              11915     15     .172573    0.1998    6494.74   0.0000

w6              11915     15    .0397685    0.2120    3228.67   0.0000

———————————————————————-

 

( 1)  [w1]_cons + [w2]_cons + [w3]_cons + [w4]_cons + [w5]_cons + [w6]_cons = 1

( 2)  [w1]LnY_riil + [w2]LnY_riil + [w3]LnY_riil + [w4]LnY_riil + [w5]LnY_riil + [w6]LnY_riil = 0

( 3)  [w6]Lnpi1est + [w6]Lnpi2est + [w6]Lnpi3est + [w6]Lnpi4est + [w6]Lnpi5est + [w6]Lnpi6est = 0

( 4)  [w5]Lnpi1est + [w5]Lnpi2est + [w5]Lnpi3est + [w5]Lnpi4est + [w5]Lnpi5est + [w5]Lnpi6est = 0

( 5)  [w4]Lnpi1est + [w4]Lnpi2est + [w4]Lnpi3est + [w4]Lnpi4est + [w4]Lnpi5est + [w4]Lnpi6est = 0

( 6)  [w3]Lnpi1est + [w3]Lnpi2est + [w3]Lnpi3est + [w3]Lnpi4est + [w3]Lnpi5est + [w3]Lnpi6est = 0

( 7)  [w2]Lnpi1est + [w2]Lnpi2est + [w2]Lnpi3est + [w2]Lnpi4est + [w2]Lnpi5est + [w2]Lnpi6est = 0

( 8)  [w1]Lnpi1est + [w1]Lnpi2est + [w1]Lnpi3est + [w1]Lnpi4est + [w1]Lnpi5est + [w1]Lnpi6est = 0

( 9)  [w5]Lnpi6est – [w6]Lnpi5est = 0

(10)  [w4]Lnpi6est – [w6]Lnpi4est = 0

(11)  [w4]Lnpi5est – [w5]Lnpi4est = 0

(12)  [w3]Lnpi6est – [w6]Lnpi3est = 0

(13)  [w3]Lnpi5est – [w5]Lnpi3est = 0

(14)  [w3]Lnpi4est – [w4]Lnpi3est = 0

(15)  [w2]Lnpi6est – [w6]Lnpi2est = 0

(16)  [w2]Lnpi5est – [w5]Lnpi2est = 0

(17)  [w2]Lnpi4est – [w4]Lnpi2est = 0

(18)  [w2]Lnpi3est – [w3]Lnpi2est = 0

(19)  [w1]Lnpi6est – [w6]Lnpi1est = 0

(20)  [w1]Lnpi5est – [w5]Lnpi1est = 0

(21)  [w1]Lnpi4est – [w4]Lnpi1est = 0

(22)  [w1]Lnpi3est – [w3]Lnpi1est = 0

(23)  [w1]Lnpi2est – [w2]Lnpi1est = 0

 

 

——————————————————————————

|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

w1           |

Lnpi1est |   .0465026   .0028973    16.05   0.000     .0408239    .0521812

Lnpi2est |   -.022103   .0015622   -14.15   0.000    -.0251648   -.0190412

Lnpi3est |    -.00646   .0013714    -4.71   0.000    -.0091479   -.0037721

Lnpi4est |   .0105814   .0015089     7.01   0.000     .0076239    .0135388

Lnpi5est |   -.021205   .0008878   -23.88   0.000    -.0229451   -.0194649

Lnpi6est |   -.007316   .0014155    -5.17   0.000    -.0100903   -.0045416

LnY_riil |  -.0363694   .0009327   -38.99   0.000    -.0381975   -.0345413

Lnage |   .0623689   .0028443    21.93   0.000     .0567942    .0679436

jk |   .0163712    .001646     9.95   0.000     .0131451    .0195974

edu |  -.0153224   .0013669   -11.21   0.000    -.0180015   -.0126432

work_i2 |   .0366072   .0019933    18.37   0.000     .0327003     .040514

work_i3 |   .0539187   .0023472    22.97   0.000     .0493182    .0585192

miskin |   .1055122   .0027407    38.50   0.000     .1001406    .1108838

wil |  -.0095976   .0014951    -6.42   0.000    -.0125278   -.0066673

milik |   .0473725   .0021775    21.76   0.000     .0431047    .0516402

IMR1 |  -.0042954   .0052883    -0.81   0.417    -.0146602    .0060694

_cons |   .0263736   .0115833     2.28   0.023     .0036708    .0490765

————-+—————————————————————-

w2           |

Lnpi1est |   -.022103   .0015622   -14.15   0.000    -.0251648   -.0190412

Lnpi2est |   .0793078   .0019458    40.76   0.000     .0754942    .0831214

Lnpi3est |  -.0051285   .0012569    -4.08   0.000     -.007592    -.002665

Lnpi4est |  -.0144565   .0011113   -13.01   0.000    -.0166345   -.0122785

Lnpi5est |  -.0193561    .001088   -17.79   0.000    -.0214885   -.0172236

Lnpi6est |  -.0182637   .0010459   -17.46   0.000    -.0203136   -.0162138

LnY_riil |    .022365   .0014022    15.95   0.000     .0196168    .0251133

Lnage |  -.0106261   .0035442    -3.00   0.003    -.0175726   -.0036796

jk |  -.0126273   .0023483    -5.38   0.000      -.01723   -.0080247

edu |   .0415636   .0019821    20.97   0.000     .0376787    .0454485

work_i2 |  -.0175694   .0027279    -6.44   0.000    -.0229159   -.0122229

work_i3 |  -.0172597   .0033459    -5.16   0.000    -.0238176   -.0107018

miskin |  -.0444202   .0038848   -11.43   0.000    -.0520342   -.0368062

wil |   .0029593   .0021777     1.36   0.174     -.001309    .0072275

milik |   .0095183   .0029578     3.22   0.001      .003721    .0153155

IMR2 |  -.1956027   .0089998   -21.73   0.000    -.2132421   -.1779634

_cons |   .0421689   .0160137     2.63   0.008     .0107826    .0735551

————-+—————————————————————-

w3           |

Lnpi1est |    -.00646   .0013714    -4.71   0.000    -.0091479   -.0037721

Lnpi2est |  -.0051285   .0012569    -4.08   0.000     -.007592    -.002665

Lnpi3est |   .0292236    .001492    19.59   0.000     .0262992     .032148

Lnpi4est |  -.0036733    .000978    -3.76   0.000    -.0055901   -.0017564

Lnpi5est |  -.0117644   .0008182   -14.38   0.000     -.013368   -.0101608

Lnpi6est |  -.0021974   .0009237    -2.38   0.017    -.0040079   -.0003869

LnY_riil |   -.011854   .0009836   -12.05   0.000    -.0137819   -.0099262

Lnage |    .039021    .002667    14.63   0.000     .0337938    .0442483

jk |  -.0182833   .0016445   -11.12   0.000    -.0215064   -.0150601

edu |   .0130299   .0013866     9.40   0.000     .0103122    .0157476

work_i2 |   .0109314   .0020524     5.33   0.000     .0069088     .014954

work_i3 |   .0205229   .0024654     8.32   0.000     .0156909    .0253549

miskin |   .0078602   .0028354     2.77   0.006      .002303    .0134174

wil |  -.0008555   .0015142    -0.56   0.572    -.0038232    .0021122

milik |   .0178162   .0021132     8.43   0.000     .0136744    .0219581

IMR3 |  -.0532986   .0083497    -6.38   0.000    -.0696637   -.0369334

_cons |     .01947   .0125029     1.56   0.119    -.0050352    .0439752

————-+—————————————————————-

w4           |

Lnpi1est |   .0105814   .0015089     7.01   0.000     .0076239    .0135388

Lnpi2est |  -.0144565   .0011113   -13.01   0.000    -.0166345   -.0122785

Lnpi3est |  -.0036733    .000978    -3.76   0.000    -.0055901   -.0017564

Lnpi4est |   .0168273   .0017963     9.37   0.000     .0133066    .0203479

Lnpi5est |  -.0098143   .0005907   -16.61   0.000    -.0109721   -.0086564

Lnpi6est |   .0005354   .0010868     0.49   0.622    -.0015947    .0026655

LnY_riil |  -.0240937   .0006456   -37.32   0.000     -.025359   -.0228284

Lnage |   .0382694   .0019547    19.58   0.000     .0344383    .0421005

jk |   .0018551   .0010879     1.71   0.088     -.000277    .0039873

edu |  -.0076636   .0009107    -8.42   0.000    -.0094485   -.0058786

work_i2 |   .0200292   .0014421    13.89   0.000     .0172027    .0228558

work_i3 |    .027897   .0016738    16.67   0.000     .0246164    .0311776

miskin |   .0423967   .0018742    22.62   0.000     .0387234    .0460701

wil |  -.0014712   .0009989    -1.47   0.141    -.0034291    .0004867

milik |   .0284236   .0015442    18.41   0.000     .0253971    .0314501

IMR4 |  -.0042899   .0037932    -1.13   0.258    -.0117243    .0031446

_cons |   .0110652   .0087282     1.27   0.205    -.0060418    .0281721

————-+—————————————————————-

w5           |

Lnpi1est |   -.021205   .0008878   -23.88   0.000    -.0229451   -.0194649

Lnpi2est |  -.0193561    .001088   -17.79   0.000    -.0214885   -.0172236

Lnpi3est |  -.0117644   .0008182   -14.38   0.000     -.013368   -.0101608

Lnpi4est |  -.0098143   .0005907   -16.61   0.000    -.0109721   -.0086564

Lnpi5est |   .0698537   .0014499    48.18   0.000     .0670119    .0726956

Lnpi6est |   -.007714   .0005693   -13.55   0.000    -.0088299   -.0065981

LnY_riil |   .0750592   .0015593    48.14   0.000     .0720031    .0781153

Lnage |   -.131319   .0048683   -26.97   0.000    -.1408606   -.1217774

jk |   .0017664   .0044163     0.40   0.689    -.0068894    .0104222

edu |   -.023837     .00367    -6.50   0.000      -.03103    -.016644

work_i2 |  -.0884986   .0046991   -18.83   0.000    -.0977087   -.0792884

work_i3 |  -.1333024   .0059169   -22.53   0.000    -.1448993   -.1217054

miskin |  -.1233628   .0074518   -16.55   0.000    -.1379681   -.1087575

wil |   .0143699   .0041482     3.46   0.001     .0062396    .0225002

milik |  -.1622661   .0049613   -32.71   0.000    -.1719901   -.1525422

IMR5 |  -.1104457   .0370584    -2.98   0.003     -.183079   -.0378125

_cons |   .8370138   .0200564    41.73   0.000     .7977039    .8763236

————-+—————————————————————-

w6           |

Lnpi1est |   -.007316   .0014155    -5.17   0.000    -.0100903   -.0045416

Lnpi2est |  -.0182637   .0010459   -17.46   0.000    -.0203136   -.0162138

Lnpi3est |  -.0021974   .0009237    -2.38   0.017    -.0040079   -.0003869

Lnpi4est |   .0005354   .0010868     0.49   0.622    -.0015947    .0026655

Lnpi5est |   -.007714   .0005693   -13.55   0.000    -.0088299   -.0065981

Lnpi6est |   .0349557   .0013233    26.42   0.000     .0323622    .0375492

LnY_riil |  -.0251071   .0006196   -40.52   0.000    -.0263216   -.0238926

Lnage |   .0246249   .0018783    13.11   0.000     .0209435    .0283063

jk |   .0020583   .0010522     1.96   0.050    -3.96e-06    .0041206

edu |  -.0064015   .0008819    -7.26   0.000      -.00813   -.0046729

work_i2 |   .0195698   .0013618    14.37   0.000     .0169007    .0222388

work_i3 |   .0254823   .0015718    16.21   0.000     .0224016     .028563

miskin |   .0198141   .0017724    11.18   0.000     .0163403    .0232879

wil |  -.0032909   .0009628    -3.42   0.001    -.0051779   -.0014038

milik |    .016134    .001426    11.31   0.000     .0133391    .0189289

IMR6 |  -.0160223   .0055736    -2.87   0.004    -.0269463   -.0050983

_cons |   .0639086   .0083219     7.68   0.000      .047598    .0802192

——————————————————————————

 

. eststo aids_ipm1

 

. esttab aids_ipm1 using $hasil\aids_ipm1.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\aids_ipm1.rtf)

 

. * menampilkan nilai rata-rata suatu variabel

. mean w1 w2 w3 w4 w5 w6

 

Mean estimation                     Number of obs    =   11915

 

————————————————————–

|       Mean   Std. Err.     [95% Conf. Interval]

————-+————————————————

w1 |    .142335   .0006826       .140997    .1436729

w2 |   .1556954   .0009545      .1538243    .1575664

w3 |   .1210816   .0006062      .1198933    .1222699

w4 |    .074494    .000434      .0736434    .0753447

w5 |   .4249856   .0017675      .4215211    .4284501

w6 |   .0814084   .0004104      .0806039    .0822129

————————————————————–

 

. sort urut

 

. tempfile final

 

. save `6kelfinal_ipm1′, replace

file D:\tesis_dewi_aids\running\02_dataproses\\dataanalisis.dta saved

 

. save $dataproses\6kelfinal_ipm1.dta, replace

file D:\tesis_dewi_aids\running\02_dataproses\\6kelfinal_ipm1.dta saved

 

 

 

. *** STEP 5,6,7. RUNNING DATA STATUS IPM=0 (RENDAH/SEDANG)

. use $dataproses\dataanalisis_ipm0.dta, clear

 

. order urut r102 r107 r108 food nfood expend exp_cap

 

. keep urut r102 r107 r108 food nfood expend exp_cap

 

. merge 1:1 urut using `wil’

(label r105 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `milik’

(label r1502 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `miskin’

(label r105 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `ipm’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `age’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `jk’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `edu’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `work’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok1′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok2′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok3′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok4′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok5′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok6′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `probit_i’

 

 

 

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. rename r102 kab

 

. rename r107 nks

 

. rename r108 urutruta

 

. count

29467

 

. sort urut

 

. order urut food wil milik work work_i1 work_i2 work_i3 miskin ipm age jk edu work kuantitas1bln Lnpi1 LnDev1 kuantitas2bln Lnpi2 LnDev2 kuan

> titas3bln Lnpi3 LnDev3 kuantitas4bln Lnpi4 LnDev4 kuantitas5bln Lnpi5 LnDev5 kuantitas6bln Lnpi6 LnDev6 Yprobit1 Yprobit2 Yprobit3 Yprobit4

> Yprobit5 Yprobit6

 

. rename food Y

 

. gen LnY=ln(Y)

 

. gen Lnage=ln(age)

 

. keep if ipm==0

(11915 observations deleted)

.

. * Data continuos

. sum Y wil milik age jk edu work_i2 work_i3 miskin Lnpi1 Lnpi2 Lnpi3 Lnpi4 Lnpi5 Lnpi6

 

Variable |       Obs        Mean    Std. Dev.       Min        Max

————-+——————————————————–

Y |     17552     1266978      837985   83485.71   1.02e+07

wil |     17552    .3591613    .4797683          0          1

milik |     17552    .9523131    .2131089          0          1

age |     17552    51.72778    13.44455         14         97

jk |     17552    .8148929    .3883955          0          1

————-+——————————————————–

edu |     17552    .1701231    .3757516          0          1

work_i2 |     17552    .4152803    .4927843          0          1

work_i3 |     17552    .4588081    .4983145          0          1

miskin |     17552    .1394143    .3463882          0          1

Lnpi1 |     17552    8.732998    1.113492          0   9.968073

————-+——————————————————–

Lnpi2 |     17552    9.532623    2.064471          0   12.07825

Lnpi3 |     17552     8.75478    1.270142          0    10.8198

Lnpi4 |     17552    8.742168    1.287536          0   10.02367

Lnpi5 |     17552     9.59532      1.2635          0   14.17219

Lnpi6 |     17552    9.698025    1.117375          0   11.75134

 

. *5. Hasil Analisis Regresi Ln Deviasi Harga

. *5.1. Kelompok 1. Padi dan umbi-umbian

. reg LnDev1 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   17552

F(  9, 17542) =   25.58

Prob > F      =  0.0000

R-squared     =  0.0367

Root MSE      =  1.0663

 

——————————————————————————

|               Robust

LnDev1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .2138184   .0186938    11.44   0.000     .1771767      .25046

Lnage |  -.0511322   .0521168    -0.98   0.327    -.1532862    .0510218

jk |  -.0660929   .0306475    -2.16   0.031     -.126165   -.0060207

edu |   .0735367   .0220645     3.33   0.001     .0302881    .1167854

work_i2 |   .2971607   .0465187     6.39   0.000     .2059794     .388342

work_i3 |   .3745374   .0441009     8.49   0.000     .2880953    .4609795

miskin |    .134918   .0217782     6.20   0.000     .0922305    .1776056

wil |  -.0503881   .0190214    -2.65   0.008    -.0876719   -.0131042

milik |   .4018241   .0702226     5.72   0.000     .2641809    .5394673

_cons |   -3.53245   .3345365   -10.56   0.000    -4.188175   -2.876725

——————————————————————————

. predict LnDev1est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      3.11    0.321097

work_i3 |      2.92    0.342149

LnY |      1.43    0.698031

Lnage |      1.27    0.786550

jk |      1.27    0.787578

edu |      1.20    0.832898

miskin |      1.19    0.843338

wil |      1.13    0.883741

milik |      1.05    0.952089

————-+———————-

Mean VIF |      1.62

 

. eststo dev1_ipm0

 

. esttab dev1_ipm0 using $hasil\dev1_ipm0.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev1_ipm0.rtf)

 

.

. *5.2. Kelompok 2. Ikan, daging, telur dan susu

. reg LnDev2 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   17552

F(  9, 17542) =   78.28

Prob > F      =  0.0000

R-squared     =  0.0775

Root MSE      =  1.9366

 

——————————————————————————

|               Robust

LnDev2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .7882185    .034306    22.98   0.000     .7209754    .8554615

Lnage |  -.2657322   .0767325    -3.46   0.001    -.4161354   -.1153289

jk |   -.131153   .0507183    -2.59   0.010    -.2305658   -.0317402

edu |   .0496663   .0365618     1.36   0.174    -.0219985    .1213311

work_i2 |    .364453   .0718654     5.07   0.000     .2235898    .5053162

work_i3 |   .4924016    .068872     7.15   0.000     .3574056    .6273977

miskin |   .0973169   .0555822     1.75   0.080    -.0116297    .2062635

wil |  -.0636342   .0324698    -1.96   0.050    -.1272783    9.86e-06

milik |   .5695431   .0955462     5.96   0.000     .3822631    .7568231

_cons |  -11.13107   .5620838   -19.80   0.000    -12.23281   -10.02933

——————————————————————————

 

. predict LnDev2est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      3.11    0.321097

work_i3 |      2.92    0.342149

LnY |      1.43    0.698031

Lnage |      1.27    0.786550

jk |      1.27    0.787578

edu |      1.20    0.832898

miskin |      1.19    0.843338

wil |      1.13    0.883741

milik |      1.05    0.952089

————-+———————-

Mean VIF |      1.62

 

. eststo dev2_ipm0

 

 

 

. esttab dev2_ipm0 using $hasil\dev2_ipm0.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev2_ipm0.rtf)

 

.

. *5.3. Kelompok 3. Sayur dan buah-buahan

. reg LnDev3 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   17552

F(  9, 17542) =   35.98

Prob > F      =  0.0000

R-squared     =  0.0477

Root MSE      =  1.1877

 

——————————————————————————

|               Robust

LnDev3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .3126619   .0216336    14.45   0.000     .2702579    .3550659

Lnage |    .032526   .0562924     0.58   0.563    -.0778127    .1428647

jk |  -.1311751   .0320379    -4.09   0.000    -.1939726   -.0683775

edu |   .0954517   .0245797     3.88   0.000     .0472729    .1436304

work_i2 |   .3662808   .0505869     7.24   0.000     .2671254    .4654362

work_i3 |   .4208256   .0478303     8.80   0.000     .3270734    .5145778

miskin |   .1626892   .0257753     6.31   0.000     .1121671    .2132113

wil |  -.0910488   .0209401    -4.35   0.000    -.1320935   -.0500041

milik |   .5065557   .0767488     6.60   0.000     .3561204    .6569911

_cons |  -5.373912   .3962797   -13.56   0.000    -6.150659   -4.597164

——————————————————————————

 

. predict LnDev3est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      3.11    0.321097

work_i3 |      2.92    0.342149

LnY |      1.43    0.698031

Lnage |      1.27    0.786550

jk |      1.27    0.787578

edu |      1.20    0.832898

miskin |      1.19    0.843338

wil |      1.13    0.883741

milik |      1.05    0.952089

————-+———————-

Mean VIF |      1.62

 

. eststo dev3_ipm0

 

. esttab dev3_ipm0 using $hasil\dev3_ipm0.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev3_ipm0.rtf)

 

. *5.4. Kelompok 4. Kacang dan minyak

. reg LnDev4 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   17552

F(  9, 17542) =   30.33

Prob > F      =  0.0000

R-squared     =  0.0462

Root MSE      =  1.2189

 

——————————————————————————

|               Robust

LnDev4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .2994164   .0221751    13.50   0.000      .255951    .3428818

Lnage |   .0425453   .0587775     0.72   0.469    -.0726644    .1577551

jk |  -.0936149   .0334134    -2.80   0.005    -.1591085   -.0281214

edu |   .0548627   .0264582     2.07   0.038     .0030021    .1067234

work_i2 |   .3814949   .0516512     7.39   0.000     .2802534    .4827365

work_i3 |   .4398689   .0482558     9.12   0.000     .3452828     .534455

miskin |   .2013236   .0253198     7.95   0.000     .1516943    .2509529

wil |  -.0846684    .021363    -3.96   0.000     -.126542   -.0427949

milik |   .5607517   .0807919     6.94   0.000     .4023915    .7191119

_cons |  -5.317055   .4090147   -13.00   0.000    -6.118764   -4.515346

——————————————————————————

. predict LnDev4est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      3.11    0.321097

work_i3 |      2.92    0.342149

LnY |      1.43    0.698031

Lnage |      1.27    0.786550

jk |      1.27    0.787578

edu |      1.20    0.832898

miskin |      1.19    0.843338

wil |      1.13    0.883741

milik |      1.05    0.952089

————-+———————-

Mean VIF |      1.62

 

. eststo dev4_ipm0

 

. esttab dev4_ipm0 using $hasil\dev4_ipm0.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev4_ipm0.rtf)

 

. *5.5. Kelompok 5. Makanan jadi dan rokok

. reg LnDev5 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   17552

F(  9, 17542) =   82.14

Prob > F      =  0.0000

R-squared     =  0.1024

Root MSE      =  1.2087

 

——————————————————————————

|               Robust

LnDev5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .5367124   .0227033    23.64   0.000     .4922116    .5812132

Lnage |  -.0209856   .0360174    -0.58   0.560    -.0915832    .0496121

jk |   .2089835   .0321854     6.49   0.000     .1458968    .2720702

edu |  -.1602536   .0188261    -8.51   0.000    -.1971546   -.1233527

work_i2 |   .0535899   .0398233     1.35   0.178    -.0244678    .1316475

work_i3 |   .1136951   .0422143     2.69   0.007     .0309509    .1964393

miskin |  -.1913722    .038345    -4.99   0.000    -.2665321   -.1162123

wil |   .0659257   .0195012     3.38   0.001     .0277015    .1041499

milik |  -.0696393   .0419265    -1.66   0.097    -.1518195    .0125408

_cons |  -7.822125   .3382387   -23.13   0.000    -8.485107   -7.159144

——————————————————————————

 

. predict LnDev5est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      3.11    0.321097

work_i3 |      2.92    0.342149

LnY |      1.43    0.698031

Lnage |      1.27    0.786550

jk |      1.27    0.787578

edu |      1.20    0.832898

miskin |      1.19    0.843338

wil |      1.13    0.883741

milik |      1.05    0.952089

————-+———————-

Mean VIF |      1.62

 

. eststo dev5_ipm0

 

. esttab dev5_ipm0 using $hasil\dev5_ipm0.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev5_ipm0.rtf)

 

.

 

 

. *5.6. Kelompok 6. Bahan Pangan Lainnya

. reg LnDev6 LnY Lnage jk edu work_i2 work_i3 miskin wil milik, f robus

 

Linear regression                                      Number of obs =   17552

F(  9, 17542) =   29.56

Prob > F      =  0.0000

R-squared     =  0.0454

Root MSE      =  1.0543

 

——————————————————————————

|               Robust

LnDev6 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .2286081    .018899    12.10   0.000     .1915642    .2656519

Lnage |   .0248184   .0567286     0.44   0.662    -.0863752    .1360121

jk |  -.0545223   .0301693    -1.81   0.071    -.1136572    .0046125

edu |   .0627691   .0218759     2.87   0.004     .0198902     .105648

work_i2 |   .3737167   .0491169     7.61   0.000     .2774427    .4699908

work_i3 |   .3697652   .0457243     8.09   0.000      .280141    .4593893

miskin |   .0785423   .0228642     3.44   0.001     .0337262    .1233584

wil |  -.0811274   .0187605    -4.32   0.000    -.1178998   -.0443551

milik |   .4924407   .0762835     6.46   0.000     .3429173     .641964

_cons |  -4.137124   .3812952   -10.85   0.000      -4.8845   -3.389748

——————————————————————————

 

. predict LnDev6est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      3.11    0.321097

work_i3 |      2.92    0.342149

LnY |      1.43    0.698031

Lnage |      1.27    0.786550

jk |      1.27    0.787578

edu |      1.20    0.832898

miskin |      1.19    0.843338

wil |      1.13    0.883741

milik |      1.05    0.952089

————-+———————-

Mean VIF |      1.62

 

. eststo dev6_ipm0

 

. esttab dev6_ipm0 using $hasil\dev6_ipm0.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev6_ipm0.rtf)

 

.

. *5.1.1. Generate Lnpiest

. gen Lnpi1est=(Lnpi1-LnDev1est) if Yprobit1==1

(271 missing values generated)

 

. replace Lnpi1est=(Lnpi1mean- LnDev1est) if Yprobit1==0

(271 real changes made)

 

. gen Lnpi2est=(Lnpi2-LnDev2est) if Yprobit2==1

(760 missing values generated)

 

. replace Lnpi2est=(Lnpi2mean- LnDev2est) if Yprobit2==0

(760 real changes made)

 

. gen Lnpi3est=(Lnpi3-LnDev3est) if Yprobit3==1

(326 missing values generated)

 

. replace Lnpi3est=(Lnpi3mean- LnDev3est) if Yprobit3==0

(326 real changes made)

 

. gen Lnpi4est=(Lnpi4-LnDev4est) if Yprobit4==1

(356 missing values generated)

 

. replace Lnpi4est=(Lnpi4mean- LnDev4est) if Yprobit4==0

(356 real changes made)

 

. gen Lnpi5est=(Lnpi5-LnDev5est) if Yprobit5==1

(190 missing values generated)

 

. replace Lnpi5est=(Lnpi5mean- LnDev5est) if Yprobit5==0

(190 real changes made)

 

. gen Lnpi6est=(Lnpi6-LnDev6est) if Yprobit6==1

(215 missing values generated)

 

. replace Lnpi6est=(Lnpi6mean- LnDev6est) if Yprobit6==0

(215 real changes made)

 

. label variable Lnpi1est “Ln unit value kel 1 hasil estimasi”

 

. label variable Lnpi2est “Ln unit value kel 2 hasil estimasi”

 

. label variable Lnpi3est “Ln unit value kel 3 hasil estimasi”

 

. label variable Lnpi4est “Ln unit value kel 4 hasil estimasi”

 

. label variable Lnpi5est “Ln unit value kel 5 hasil estimasi”

 

. label variable Lnpi6est “Ln unit value kel 6 hasil estimasi”

 

. sort urut

 

. *5.1.2. Generate Variabel Budget Share kelompok komoditi ke-i(wi)

. gen w1 = (harga1bln/Y)

 

. gen w2 = (harga2bln/Y)

 

. gen w3 = (harga3bln/Y)

 

. gen w4 = (harga4bln/Y)

 

. gen w5 = (harga5bln/Y)

 

. gen w6 = (harga6bln/Y)

 

. replace w1=0 if w1==.

(0 real changes made)

 

. replace w2=0 if w2==.

(0 real changes made)

 

. replace w3=0 if w3==.

(0 real changes made)

 

. replace w4=0 if w4==.

(0 real changes made)

 

. replace w5=0 if w5==.

(0 real changes made)

 

. replace w6=0 if w6==.

(0 real changes made)

 

. label variable w1 “budget share kel 1”

 

. label variable w2 “budget share kel 2”

 

. label variable w3 “budget share kel 3”

 

. label variable w4 “budget share kel 4”

 

. label variable w5 “budget share kel 5”

 

. label variable w6 “budget share kel 6”

 

. count

17552

 

. sort urut

 

.

 

 

. *6. Analisis Regresi Probit

. *6.1. Kelompok 1. Padi dan umbi-umbian

. probit Yprobit1 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood =  -1399.185

Iteration 1:   log likelihood = -1149.4447

Iteration 2:   log likelihood = -1112.6459

Iteration 3:   log likelihood =  -1112.063

Iteration 4:   log likelihood = -1112.0629

Iteration 5:   log likelihood = -1112.0629

 

Probit regression                                 Number of obs   =      17552

LR chi2(15)     =     574.24

Prob > chi2     =     0.0000

Log likelihood = -1112.0629                       Pseudo R2       =     0.2052

 

——————————————————————————

Yprobit1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   1.557899   .1474279    10.57   0.000     1.268946    1.846852

Lnpi2est |  -.0462065   .0827972    -0.56   0.577    -.2084861    .1160731

Lnpi3est |  -.1010244   .0755695    -1.34   0.181    -.2491379    .0470892

Lnpi4est |   .4762444   .1206739     3.95   0.000     .2397279    .7127609

Lnpi5est |  -.0713228     .03715    -1.92   0.055    -.1441355    .0014899

Lnpi6est |   -.139515   .1111064    -1.26   0.209    -.3572797    .0782496

LnY |   .6228844   .0693369     8.98   0.000     .4869866    .7587822

Lnage |  -.2455639   .1073033    -2.29   0.022    -.4558745   -.0352533

jk |  -.1820144   .0704207    -2.58   0.010    -.3200364   -.0439925

edu |   .1040704   .0841937     1.24   0.216    -.0609462    .2690871

work_i2 |   .9680072    .090736    10.67   0.000      .790168    1.145846

work_i3 |   1.599607   .1096544    14.59   0.000     1.384689    1.814526

miskin |   1.248698   .1259682     9.91   0.000     1.001805    1.495591

wil |  -.1693735   .0584588    -2.90   0.004    -.2839507   -.0547963

milik |   1.421137   .1189671    11.95   0.000     1.187966    1.654308

_cons |  -22.45558   2.203257   -10.19   0.000    -26.77389   -18.13728

——————————————————————————

 

. outreg2 using $hasil\probit1_ipm0,dec(3) replace

dir : seeout

 

. predict probitx1, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      17552

Model VCE    : OIM

 

Expression   : Pr(Yprobit1), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0497498   .0051206     9.72   0.000     .0397137    .0597859

Lnpi2est |  -.0014756   .0026444    -0.56   0.577    -.0066585    .0037074

Lnpi3est |  -.0032261   .0024163    -1.34   0.182    -.0079619    .0015097

Lnpi4est |   .0152083   .0039121     3.89   0.000     .0075407     .022876

Lnpi5est |  -.0022776   .0011897    -1.91   0.056    -.0046094    .0000542

Lnpi6est |  -.0044553   .0035523    -1.25   0.210    -.0114177    .0025071

LnY |   .0198911   .0023757     8.37   0.000     .0152349    .0245473

Lnage |  -.0078418   .0034429    -2.28   0.023    -.0145897   -.0010939

jk |  -.0058124   .0022636    -2.57   0.010    -.0102491   -.0013758

edu |   .0033234   .0026924     1.23   0.217    -.0019536    .0086003

work_i2 |   .0309122    .003175     9.74   0.000     .0246893    .0371352

work_i3 |   .0510817   .0041302    12.37   0.000     .0429867    .0591767

miskin |   .0398758    .004373     9.12   0.000     .0313049    .0484467

wil |  -.0054088   .0018808    -2.88   0.004    -.0090951   -.0017224

milik |   .0453824   .0042542    10.67   0.000     .0370444    .0537205

——————————————————————————

 

. outreg2 using $hasil\probit1_mfx_ipm0.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit1_mfx_ipm0.xls

dir : seeout

 

. *6.2. Kelompok 2. Ikan, daging, telur dan susu

. probit Yprobit2 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -3129.4029

Iteration 1:   log likelihood = -2648.5314

Iteration 2:   log likelihood = -2612.5893

Iteration 3:   log likelihood = -2612.4424

Iteration 4:   log likelihood = -2612.4424

 

Probit regression                                 Number of obs   =      17552

LR chi2(15)     =    1033.92

Prob > chi2     =     0.0000

Log likelihood = -2612.4424                       Pseudo R2       =     0.1652

 

——————————————————————————

Yprobit2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .1438848   .1062126     1.35   0.176    -.0642882    .3520577

Lnpi2est |   .1862942    .058837     3.17   0.002     .0709759    .3016126

Lnpi3est |   -.211544   .0497165    -4.26   0.000    -.3089866   -.1141015

Lnpi4est |   .5857478   .0809607     7.23   0.000     .4270677    .7444279

Lnpi5est |  -.1257402   .0215732    -5.83   0.000    -.1680229   -.0834575

Lnpi6est |   .2037703   .0752405     2.71   0.007     .0563017     .351239

LnY |   .9177462   .0513201    17.88   0.000     .8171607    1.018332

Lnage |  -.2477301   .0765173    -3.24   0.001    -.3977013    -.097759

jk |  -.1706404   .0490909    -3.48   0.001    -.2668567    -.074424

edu |   .0061596   .0620029     0.10   0.921    -.1153639    .1276832

work_i2 |   .5917986   .0660976     8.95   0.000     .4622498    .7213475

work_i3 |   .8172603    .071004    11.51   0.000     .6780949    .9564256

miskin |   .4030036   .0584775     6.89   0.000     .2883898    .5176173

wil |  -.0801951   .0410891    -1.95   0.051    -.1607283    .0003381

milik |   .9770464   .0900049    10.86   0.000       .80064    1.153453

_cons |  -18.60874   1.565875   -11.88   0.000     -21.6778   -15.53969

——————————————————————————

 

. outreg2 using $hasil\probit2_ipm0,dec(3) replace

dir : seeout

 

. predict probitx2, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      17552

Model VCE    : OIM

 

Expression   : Pr(Yprobit2), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0112274   .0082913     1.35   0.176    -.0050232     .027478

Lnpi2est |   .0145367   .0045988     3.16   0.002     .0055232    .0235501

Lnpi3est |  -.0165069     .00389    -4.24   0.000    -.0241313   -.0088826

Lnpi4est |   .0457063   .0063797     7.16   0.000     .0332022    .0582104

Lnpi5est |  -.0098116   .0016905    -5.80   0.000    -.0131248   -.0064983

Lnpi6est |   .0159003   .0058797     2.70   0.007     .0043763    .0274244

LnY |   .0716124   .0042513    16.84   0.000       .06328    .0799447

Lnage |  -.0193306   .0059799    -3.23   0.001    -.0310509   -.0076102

jk |  -.0133152   .0038425    -3.47   0.001    -.0208464   -.0057839

edu |   .0004806   .0048382     0.10   0.921     -.009002    .0099633

work_i2 |   .0461784   .0052337     8.82   0.000     .0359206    .0564363

work_i3 |   .0637714   .0056703    11.25   0.000     .0526578     .074885

miskin |   .0314466   .0046123     6.82   0.000     .0224066    .0404866

wil |  -.0062577   .0032085    -1.95   0.051    -.0125462    .0000309

milik |   .0762396   .0071742    10.63   0.000     .0621784    .0903008

——————————————————————————

 

. outreg2 using $hasil\probit2_mfx_ipm0.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit2_mfx_ipm0.xls

dir : seeout

 

.

 

 

. *6.3. Kelompok 3. Sayur dan buah-buahan

. probit Yprobit3 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood =  -1622.398

Iteration 1:   log likelihood = -1359.7971

Iteration 2:   log likelihood = -1327.8656

Iteration 3:   log likelihood = -1327.4891

Iteration 4:   log likelihood = -1327.4886

Iteration 5:   log likelihood = -1327.4886

 

Probit regression                                 Number of obs   =      17552

LR chi2(15)     =     589.82

Prob > chi2     =     0.0000

Log likelihood = -1327.4886                       Pseudo R2       =     0.1818

 

——————————————————————————

Yprobit3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .4384737   .1422607     3.08   0.002     .1596478    .7172995

Lnpi2est |   .0566502   .0788536     0.72   0.472       -.0979    .2112003

Lnpi3est |   .2417499   .0723358     3.34   0.001     .0999744    .3835255

Lnpi4est |    .295808    .111085     2.66   0.008     .0780854    .5135305

Lnpi5est |  -.1178519   .0314218    -3.75   0.000    -.1794374   -.0562664

Lnpi6est |  -.1126258   .1013951    -1.11   0.267    -.3113566    .0861051

LnY |   .7426763    .066376    11.19   0.000     .6125818    .8727708

Lnage |  -.0055054   .0974253    -0.06   0.955    -.1964555    .1854447

jk |  -.3054609   .0675098    -4.52   0.000    -.4377777    -.173144

edu |   .0836133   .0781287     1.07   0.285    -.0695162    .2367428

work_i2 |   .8176815   .0834053     9.80   0.000       .65421    .9811529

work_i3 |    1.17925   .0955733    12.34   0.000     .9919303    1.366571

miskin |   1.136263   .1129165    10.06   0.000     .9149503    1.357575

wil |  -.1943694    .053953    -3.60   0.000    -.3001154   -.0886234

milik |   1.128561   .1068765    10.56   0.000     .9190873    1.338035

_cons |  -16.80561   2.007237    -8.37   0.000    -20.73972    -12.8715

——————————————————————————

 

. outreg2 using $hasil\probit3_ipm0,dec(3) replace

dir : seeout

 

. predict probitx3, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      17552

Model VCE    : OIM

 

Expression   : Pr(Yprobit3), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0168494   .0055038     3.06   0.002     .0060622    .0276366

Lnpi2est |   .0021769   .0030317     0.72   0.473    -.0037652     .008119

Lnpi3est |   .0092898   .0028015     3.32   0.001     .0037989    .0147807

Lnpi4est |   .0113671   .0042931     2.65   0.008     .0029528    .0197814

Lnpi5est |  -.0045287   .0012182    -3.72   0.000    -.0069163   -.0021412

Lnpi6est |  -.0043279    .003899    -1.11   0.267    -.0119699    .0033141

LnY |   .0285391   .0027857    10.24   0.000     .0230793    .0339989

Lnage |  -.0002116   .0037437    -0.06   0.955    -.0075492     .007126

jk |  -.0117381   .0026346    -4.46   0.000    -.0169018   -.0065744

edu |    .003213    .003005     1.07   0.285    -.0026767    .0091028

work_i2 |   .0314214   .0034259     9.17   0.000     .0247068    .0381359

work_i3 |   .0453155   .0040761    11.12   0.000     .0373264    .0533046

miskin |   .0436636   .0046591     9.37   0.000      .034532    .0527952

wil |  -.0074691   .0020934    -3.57   0.000    -.0115721   -.0033661

milik |   .0433677   .0044275     9.80   0.000     .0346899    .0520454

——————————————————————————

 

. outreg2 using $hasil\probit3_mfx_ipm0.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit3_mfx_ipm0.xls

dir : seeout

 

.

. *6.4. Kelompok 4. Kacang dan minyak

. probit Yprobit4 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -1740.0504

Iteration 1:   log likelihood =  -1437.759

Iteration 2:   log likelihood = -1401.0321

Iteration 3:   log likelihood = -1400.4511

Iteration 4:   log likelihood = -1400.4509

Iteration 5:   log likelihood = -1400.4509

 

Probit regression                                 Number of obs   =      17552

LR chi2(15)     =     679.20

Prob > chi2     =     0.0000

Log likelihood = -1400.4509                       Pseudo R2       =     0.1952

 

——————————————————————————

Yprobit4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |    .590837    .138247     4.27   0.000     .3198779    .8617962

Lnpi2est |  -.0444288   .0742231    -0.60   0.549    -.1899034    .1010458

Lnpi3est |  -.1349269   .0670272    -2.01   0.044    -.2662978   -.0035561

Lnpi4est |   1.024804   .1106865     9.26   0.000     .8078626    1.241746

Lnpi5est |  -.0670508   .0326451    -2.05   0.040     -.131034   -.0030677

Lnpi6est |  -.2578652    .098328    -2.62   0.009    -.4505845   -.0651458

LnY |   .6941402   .0619459    11.21   0.000     .5727286    .8155519

Lnage |  -.0344037    .095599    -0.36   0.719    -.2217743     .152967

jk |  -.1863382   .0644634    -2.89   0.004    -.3126841   -.0599923

edu |  -.0026946    .072685    -0.04   0.970    -.1451545    .1397654

work_i2 |   .8689293   .0824884    10.53   0.000      .707255    1.030604

work_i3 |   1.298757   .0955774    13.59   0.000     1.111429    1.486086

miskin |   1.253383   .1149376    10.90   0.000     1.028109    1.478656

wil |  -.1994313   .0525675    -3.79   0.000    -.3024617    -.096401

milik |   1.293948   .1058951    12.22   0.000     1.086398    1.501499

_cons |  -18.94225   1.960431    -9.66   0.000    -22.78463   -15.09988

——————————————————————————

 

. outreg2 using $hasil\probit4_ipm0,dec(3) replace

dir : seeout

 

. predict probitx4, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      17552

Model VCE    : OIM

 

Expression   : Pr(Yprobit4), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0240856   .0057008     4.22   0.000     .0129122    .0352591

Lnpi2est |  -.0018112   .0030262    -0.60   0.550    -.0077424    .0041201

Lnpi3est |  -.0055003   .0027388    -2.01   0.045    -.0108682   -.0001325

Lnpi4est |   .0417764   .0047463     8.80   0.000     .0324739     .051079

Lnpi5est |  -.0027333   .0013338    -2.05   0.040    -.0053475   -.0001192

Lnpi6est |  -.0105119   .0040256    -2.61   0.009    -.0184019    -.002622

LnY |   .0282968   .0027244    10.39   0.000     .0229571    .0336366

Lnage |  -.0014025   .0038971    -0.36   0.719    -.0090406    .0062356

jk |  -.0075961   .0026434    -2.87   0.004     -.012777   -.0024152

edu |  -.0001098    .002963    -0.04   0.970    -.0059173    .0056976

work_i2 |   .0354222   .0035871     9.87   0.000     .0283916    .0424527

work_i3 |   .0529442   .0043376    12.21   0.000     .0444427    .0614457

miskin |   .0510945   .0050331    10.15   0.000     .0412298    .0609593

wil |  -.0081299   .0021631    -3.76   0.000    -.0123696   -.0038902

milik |   .0527482   .0046945    11.24   0.000      .043547    .0619493

——————————————————————————

 

. outreg2 using $hasil\probit4_mfx_ipm0.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit4_mfx_ipm0.xls

dir : seeout

 

.

. *6.5. Kelompok 5. Makanan jadi dan rokok

. probit Yprobit5 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -1048.8887

Iteration 1:   log likelihood = -734.01168

Iteration 2:   log likelihood = -661.10787

Iteration 3:   log likelihood = -657.31273

Iteration 4:   log likelihood = -657.29371

Iteration 5:   log likelihood = -657.29371

 

Probit regression                                 Number of obs   =      17552

LR chi2(15)     =     783.19

Prob > chi2     =     0.0000

Log likelihood = -657.29371                       Pseudo R2       =     0.3733

 

——————————————————————————

Yprobit5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .2718409   .1964462     1.38   0.166    -.1131866    .6568684

Lnpi2est |  -.2865174   .1029693    -2.78   0.005    -.4883335   -.0847013

Lnpi3est |  -.1048129   .0905677    -1.16   0.247    -.2823223    .0726966

Lnpi4est |   -.244003   .1502351    -1.62   0.104    -.5384583    .0504524

Lnpi5est |  -.4519562   .0302958   -14.92   0.000    -.5113348   -.3925775

Lnpi6est |  -.3022519   .1363523    -2.22   0.027    -.5694974   -.0350063

LnY |   .6813011   .0950332     7.17   0.000     .4950395    .8675628

Lnage |  -.1781895   .1639726    -1.09   0.277    -.4995699    .1431908

jk |   .0348409   .0892102     0.39   0.696     -.140008    .2096897

edu |   -.267129   .1563059    -1.71   0.087     -.573483     .039225

work_i2 |  -.0373021   .1366424    -0.27   0.785    -.3051163    .2305122

work_i3 |  -.2413589    .133011    -1.81   0.070    -.5020557    .0193379

miskin |  -.0611416   .1004458    -0.61   0.543    -.2580117    .1357285

wil |   .0709745   .0864061     0.82   0.411    -.0983784    .2403274

milik |   -.446008   .2158211    -2.07   0.039    -.8690095   -.0230064

_cons |   6.149517   2.965886     2.07   0.038     .3364872    11.96255

——————————————————————————

 

. outreg2 using $hasil\probit5_ipm0,dec(3) replace

dir : seeout

 

. predict probitx5, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      17552

Model VCE    : OIM

 

Expression   : Pr(Yprobit5), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0051822    .003752     1.38   0.167    -.0021716     .012536

Lnpi2est |   -.005462   .0019786    -2.76   0.006      -.00934   -.0015839

Lnpi3est |  -.0019981   .0017288    -1.16   0.248    -.0053864    .0013902

Lnpi4est |  -.0046515    .002872    -1.62   0.105    -.0102806    .0009776

Lnpi5est |  -.0086158   .0006856   -12.57   0.000    -.0099596   -.0072721

Lnpi6est |  -.0057619   .0026148    -2.20   0.028    -.0108869    -.000637

LnY |   .0129879   .0019224     6.76   0.000     .0092201    .0167557

Lnage |  -.0033969   .0031307    -1.09   0.278    -.0095329    .0027392

jk |   .0006642   .0017017     0.39   0.696     -.002671    .0039994

edu |  -.0050924   .0029885    -1.70   0.088    -.0109497    .0007649

work_i2 |  -.0007111   .0026051    -0.27   0.785    -.0058171    .0043949

work_i3 |  -.0046011   .0025461    -1.81   0.071    -.0095914    .0003892

miskin |  -.0011656   .0019148    -0.61   0.543    -.0049184    .0025873

wil |    .001353   .0016491     0.82   0.412    -.0018792    .0045853

milik |  -.0085024   .0041312    -2.06   0.040    -.0165994   -.0004054

——————————————————————————

 

. outreg2 using $hasil\probit5_mfx_ipm0.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit5_mfx_ipm0.xls

dir : seeout

 

.

. *6.6. Kelompok 6. Bahan Pangan Lainnya

. probit Yprobit6 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

Iteration 0:   log likelihood = -1160.1691

Iteration 1:   log likelihood = -928.66403

Iteration 2:   log likelihood = -891.29944

Iteration 3:   log likelihood = -890.86125

Iteration 4:   log likelihood = -890.86116

 

Probit regression                                 Number of obs   =      17552

LR chi2(15)     =     538.62

Prob > chi2     =     0.0000

Log likelihood = -890.86116                       Pseudo R2       =     0.2321

 

——————————————————————————

Yprobit6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .6504585   .1680582     3.87   0.000     .3210704    .9798465

Lnpi2est |   -.042237   .0919079    -0.46   0.646     -.222373    .1378991

Lnpi3est |   -.022945    .084896    -0.27   0.787    -.1893381    .1434481

Lnpi4est |   .6203151   .1362776     4.55   0.000     .3532159    .8874143

Lnpi5est |  -.0223786   .0413598    -0.54   0.588    -.1034424    .0586852

Lnpi6est |   .6485948   .1346692     4.82   0.000      .384648    .9125415

LnY |   .6897111   .0778635     8.86   0.000     .5371015    .8423207

Lnage |  -.0632979   .1168154    -0.54   0.588    -.2922519    .1656562

jk |  -.1237871   .0772934    -1.60   0.109    -.2752795    .0277053

edu |   .0561117   .0960838     0.58   0.559    -.1322092    .2444325

work_i2 |   1.292925   .1044144    12.38   0.000     1.088276    1.497573

work_i3 |   1.504584    .115068    13.08   0.000     1.279055    1.730113

miskin |   1.159545   .1265926     9.16   0.000     .9114281    1.407662

wil |  -.2494582   .0651956    -3.83   0.000    -.3772392   -.1216771

milik |   1.734759   .1313607    13.21   0.000     1.477296    1.992221

_cons |  -26.58739   2.496627   -10.65   0.000    -31.48069   -21.69409

——————————————————————————

 

. outreg2 using $hasil\probit6_ipm0,dec(3) replace

dir : seeout

 

. predict probitx6, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      17552

Model VCE    : OIM

 

Expression   : Pr(Yprobit6), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0166105   .0043666     3.80   0.000     .0080522    .0251689

Lnpi2est |  -.0010786   .0023474    -0.46   0.646    -.0056794    .0035222

Lnpi3est |  -.0005859   .0021681    -0.27   0.787    -.0048353    .0036634

Lnpi4est |   .0158408   .0035674     4.44   0.000     .0088487    .0228328

Lnpi5est |  -.0005715   .0010564    -0.54   0.589    -.0026421    .0014991

Lnpi6est |   .0165629   .0035287     4.69   0.000     .0096468    .0234791

LnY |   .0176129   .0021758     8.09   0.000     .0133485    .0218774

Lnage |  -.0016164   .0029843    -0.54   0.588    -.0074656    .0042328

jk |  -.0031611   .0019816    -1.60   0.111    -.0070449    .0007227

edu |   .0014329   .0024547     0.58   0.559    -.0033782     .006244

work_i2 |    .033017    .003112    10.61   0.000     .0269176    .0391163

work_i3 |    .038422    .003497    10.99   0.000     .0315681     .045276

miskin |   .0296109   .0035498     8.34   0.000     .0226534    .0365684

wil |  -.0063703   .0016939    -3.76   0.000    -.0096904   -.0030503

milik |   .0442999   .0039794    11.13   0.000     .0365005    .0520994

——————————————————————————

 

. outreg2 using $hasil\probit6_mfx_ipm0.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit6_mfx_ipm0.xls

dir : seeout

 

.

 

 

. *6.1.1. Generate Invers Mills RAtio (IMR)

. gen IMR1 = (normalden( probitx1)/normprob( probitx1))

 

. gen IMR2 = (normalden( probitx2)/normprob( probitx2))

 

. gen IMR3 = (normalden( probitx3)/normprob( probitx3))

 

. gen IMR4 = (normalden( probitx4)/normprob( probitx4))

 

. gen IMR5 = (normalden( probitx5)/normprob( probitx5))

 

. gen IMR6 = (normalden( probitx6)/normprob( probitx6))

 

. label variable IMR1 “Inverse Mills Ratio Kel 1”

 

. label variable IMR2 “Inverse Mills Ratio Kel 2”

 

. label variable IMR3 “Inverse Mills Ratio Kel 3”

 

. label variable IMR4 “Inverse Mills Ratio Kel 4”

 

. label variable IMR5 “Inverse Mills Ratio Kel 5”

 

. label variable IMR6 “Inverse Mills Ratio Kel 6”

 

.

. *6.1.2. Generate Indeks Harga Stone

. gen LnIndexStone=((w1*Lnpi1est)+(w2*Lnpi2est)+(w3*Lnpi3est)+(w4*Lnpi4est)+(w5*Lnpi5est)+(w6*Lnpi6est))

 

. gen LnY_riil = (LnY- LnIndexStone)

 

. label variable LnIndexStone “Ln Indeks harga Stone”

 

. label variable LnY_riil “Ln Pengeluaran riil”

 

. *7. Analisis Regresi Dengan Menerapkan Restriksi Permintaan (LA_AIDS Model)

. **  Menerapkan Restriksi Permintaan yang terdiri atas :

. *         1. Simetri

. *         2. Adding Up

. *         3. Homogeneity

.

. constraint define 1 [w1]Lnpi2est=[w2]Lnpi1est

 

. constraint define 2 [w1]Lnpi3est=[w3]Lnpi1est

 

. constraint define 3 [w1]Lnpi4est=[w4]Lnpi1est

 

. constraint define 4 [w1]Lnpi5est=[w5]Lnpi1est

 

. constraint define 5 [w1]Lnpi6est=[w6]Lnpi1est

 

. constraint define 6 [w2]Lnpi3est=[w3]Lnpi2est

 

. constraint define 7 [w2]Lnpi4est=[w4]Lnpi2est

 

. constraint define 8 [w2]Lnpi5est=[w5]Lnpi2est

 

. constraint define 9 [w2]Lnpi6est=[w6]Lnpi2est

 

. constraint define 10 [w3]Lnpi4est=[w4]Lnpi3est

 

. constraint define 11 [w3]Lnpi5est=[w5]Lnpi3est

 

. constraint define 12 [w3]Lnpi6est=[w6]Lnpi3est

 

. constraint define 13 [w4]Lnpi5est=[w5]Lnpi4est

 

. constraint define 14 [w4]Lnpi6est=[w6]Lnpi4est

 

. constraint define 15 [w5]Lnpi6est=[w6]Lnpi5est

 

. constraint define 16 [w1]Lnpi1est=-[w1]Lnpi2est-[w1]Lnpi3est-[w1]Lnpi4est-[w1]Lnpi5est-[w1]Lnpi6est

 

. constraint define 17 [w2]Lnpi2est=-[w2]Lnpi1est-[w2]Lnpi3est-[w2]Lnpi4est-[w2]Lnpi5est-[w2]Lnpi6est

 

. constraint define 18 [w3]Lnpi3est=-[w3]Lnpi1est-[w3]Lnpi2est-[w3]Lnpi4est-[w3]Lnpi5est-[w3]Lnpi6est

 

. constraint define 19 [w4]Lnpi4est=-[w4]Lnpi1est-[w4]Lnpi2est-[w4]Lnpi3est-[w4]Lnpi5est-[w4]Lnpi6est

 

. constraint define 20 [w5]Lnpi5est=-[w5]Lnpi1est-[w5]Lnpi2est-[w5]Lnpi3est-[w5]Lnpi4est-[w5]Lnpi6est

 

. constraint define 21 [w6]Lnpi6est=-[w6]Lnpi1est-[w6]Lnpi2est-[w6]Lnpi3est-[w6]Lnpi4est-[w6]Lnpi5est

 

. constraint 22 [w1]LnY_riil=-[w2]LnY_riil-[w3]LnY_riil-[w4]LnY_riil-[w5]LnY_riil-[w6]LnY_riil

 

. constraint 23 [w1]_cons+[w2]_cons+[w3]_cons+[w4]_cons+[w5]_cons+[w6]_cons=1

 

. set matsize 800

 

. reg3 (w1 = Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est ///

> LnY_riil Lnage jk edu work_i2 work_i3 miskin wil milik IMR1) (w2=Lnpi1est ///

> Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk edu work_i2 ///

> work_i3 miskin wil milik IMR2) (w3=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est ///

> Lnpi6est LnY_riil Lnage jk edu work_i2 work_i3 miskin wil milik IMR3) ///

> (w4=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk ///

> edu work_i2 work_i3 miskin wil milik IMR4) (w5=Lnpi1est Lnpi2est Lnpi3est ///

> Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk edu work_i2 work_i3 miskin wil ///

> milik IMR5) (w6=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil ///

> Lnage jk edu work_i2 work_i3 miskin wil milik IMR6) , constraints(23 22 21 20 ///

> 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1) ireg3 nodfk sure corr(independent)

 

Iteration 1:   tolerance =  .05095346

Iteration 2:   tolerance =   .0006242

Iteration 3:   tolerance =  .00001679

Iteration 4:   tolerance =  6.813e-07

 

Seemingly unrelated regression, iterated

———————————————————————-

Equation          Obs  Parms        RMSE    “R-sq”       chi2        P

———————————————————————-

w1              17552     15    .0735898    0.2706    6819.89   0.0000

w2              17552     15    .0804086    0.1726    4273.75   0.0000

w3              17552     15    .0599249    0.0990    2067.62   0.0000

w4              17552     15    .0439955    0.1719    3411.19   0.0000

w5              17552     15    .1538155    0.2206    7981.32   0.0000

w6              17552     15    .0435155    0.1411    3038.57   0.0000

———————————————————————-

 

( 1)  [w1]_cons + [w2]_cons + [w3]_cons + [w4]_cons + [w5]_cons + [w6]_cons = 1

( 2)  [w1]LnY_riil + [w2]LnY_riil + [w3]LnY_riil + [w4]LnY_riil + [w5]LnY_riil + [w6]LnY_riil = 0

( 3)  [w6]Lnpi1est + [w6]Lnpi2est + [w6]Lnpi3est + [w6]Lnpi4est + [w6]Lnpi5est + [w6]Lnpi6est = 0

( 4)  [w5]Lnpi1est + [w5]Lnpi2est + [w5]Lnpi3est + [w5]Lnpi4est + [w5]Lnpi5est + [w5]Lnpi6est = 0

( 5)  [w4]Lnpi1est + [w4]Lnpi2est + [w4]Lnpi3est + [w4]Lnpi4est + [w4]Lnpi5est + [w4]Lnpi6est = 0

( 6)  [w3]Lnpi1est + [w3]Lnpi2est + [w3]Lnpi3est + [w3]Lnpi4est + [w3]Lnpi5est + [w3]Lnpi6est = 0

( 7)  [w2]Lnpi1est + [w2]Lnpi2est + [w2]Lnpi3est + [w2]Lnpi4est + [w2]Lnpi5est + [w2]Lnpi6est = 0

( 8)  [w1]Lnpi1est + [w1]Lnpi2est + [w1]Lnpi3est + [w1]Lnpi4est + [w1]Lnpi5est + [w1]Lnpi6est = 0

( 9)  [w5]Lnpi6est – [w6]Lnpi5est = 0

(10)  [w4]Lnpi6est – [w6]Lnpi4est = 0

(11)  [w4]Lnpi5est – [w5]Lnpi4est = 0

(12)  [w3]Lnpi6est – [w6]Lnpi3est = 0

(13)  [w3]Lnpi5est – [w5]Lnpi3est = 0

(14)  [w3]Lnpi4est – [w4]Lnpi3est = 0

(15)  [w2]Lnpi6est – [w6]Lnpi2est = 0

(16)  [w2]Lnpi5est – [w5]Lnpi2est = 0

(17)  [w2]Lnpi4est – [w4]Lnpi2est = 0

(18)  [w2]Lnpi3est – [w3]Lnpi2est = 0

(19)  [w1]Lnpi6est – [w6]Lnpi1est = 0

(20)  [w1]Lnpi5est – [w5]Lnpi1est = 0

(21)  [w1]Lnpi4est – [w4]Lnpi1est = 0

(22)  [w1]Lnpi3est – [w3]Lnpi1est = 0

(23)  [w1]Lnpi2est – [w2]Lnpi1est = 0

 

——————————————————————————

|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

w1           |

Lnpi1est |   .0597647   .0026202    22.81   0.000     .0546292    .0649002

Lnpi2est |  -.0196573   .0013853   -14.19   0.000    -.0223723   -.0169422

Lnpi3est |  -.0153527   .0012001   -12.79   0.000    -.0177048   -.0130006

Lnpi4est |   .0047722   .0013724     3.48   0.001     .0020823    .0074622

Lnpi5est |  -.0124223   .0007894   -15.74   0.000    -.0139695   -.0108751

Lnpi6est |  -.0171047   .0013405   -12.76   0.000    -.0197321   -.0144772

LnY_riil |  -.0284461   .0010306   -27.60   0.000     -.030466   -.0264262

Lnage |   .0063155   .0022112     2.86   0.004     .0019815    .0106494

jk |  -.0007434    .001594    -0.47   0.641    -.0038676    .0023807

edu |  -.0171824    .001625   -10.57   0.000    -.0203673   -.0139974

work_i2 |   .0159631   .0021504     7.42   0.000     .0117484    .0201778

work_i3 |   .0374669   .0021972    17.05   0.000     .0331605    .0417733

miskin |   .1009175   .0017818    56.64   0.000     .0974252    .1044097

wil |  -.0150367   .0012501   -12.03   0.000    -.0174869   -.0125866

milik |   .0362636     .00292    12.42   0.000     .0305405    .0419868

IMR1 |   .1522517   .0126854    12.00   0.000     .1273888    .1771145

_cons |   .2768928   .0103334    26.80   0.000     .2566397     .297146

————-+—————————————————————-

w2           |

Lnpi1est |  -.0196573   .0013853   -14.19   0.000    -.0223723   -.0169422

Lnpi2est |   .0325451   .0014426    22.56   0.000     .0297177    .0353725

Lnpi3est |   .0095878   .0009387    10.21   0.000     .0077481    .0114276

Lnpi4est |     .00605   .0008998     6.72   0.000     .0042865    .0078136

Lnpi5est |  -.0231257   .0007679   -30.12   0.000    -.0246308   -.0216207

Lnpi6est |  -.0053999   .0008742    -6.18   0.000    -.0071134   -.0036865

LnY_riil |   .0008085   .0012102     0.67   0.504    -.0015635    .0031804

Lnage |  -.0013888   .0024294    -0.57   0.568    -.0061503    .0033727

jk |  -.0244696   .0017372   -14.09   0.000    -.0278745   -.0210647

edu |   .0411321   .0017635    23.32   0.000     .0376757    .0445885

work_i2 |  -.0089422   .0022522    -3.97   0.000    -.0133564   -.0045279

work_i3 |  -.0124652   .0022216    -5.61   0.000    -.0168194    -.008111

miskin |  -.0138804   .0018676    -7.43   0.000    -.0175408   -.0102201

wil |   .0018392   .0013578     1.35   0.176    -.0008221    .0045005

milik |   .0086272   .0030752     2.81   0.005     .0025998    .0146545

IMR2 |   -.240966   .0092278   -26.11   0.000    -.2590522   -.2228798

_cons |   .1536986   .0109674    14.01   0.000      .132203    .1751942

————-+—————————————————————-

w3           |

Lnpi1est |  -.0153527   .0012001   -12.79   0.000    -.0177048   -.0130006

Lnpi2est |   .0095878   .0009387    10.21   0.000     .0077481    .0114276

Lnpi3est |   .0238559    .001164    20.49   0.000     .0215745    .0261374

Lnpi4est |  -.0009457    .000805    -1.17   0.240    -.0025234    .0006321

Lnpi5est |  -.0159164   .0006158   -25.85   0.000    -.0171234   -.0147095

Lnpi6est |  -.0012289   .0007908    -1.55   0.120    -.0027788     .000321

LnY_riil |   .0001924     .00086     0.22   0.823    -.0014932     .001878

Lnage |     .02388   .0018255    13.08   0.000     .0203021     .027458

jk |  -.0238284   .0013122   -18.16   0.000    -.0264003   -.0212565

edu |    .013449   .0013219    10.17   0.000      .010858    .0160399

work_i2 |    .012172    .001839     6.62   0.000     .0085676    .0157763

work_i3 |   .0177101   .0018581     9.53   0.000     .0140684    .0213518

miskin |   .0015154   .0015087     1.00   0.315    -.0014417    .0044725

wil |  -.0032855   .0010285    -3.19   0.001    -.0053014   -.0012697

milik |    .022583   .0024991     9.04   0.000     .0176849    .0274812

IMR3 |   .0615611   .0118889     5.18   0.000     .0382593     .084863

_cons |   .0106828   .0089283     1.20   0.231    -.0068163     .028182

————-+—————————————————————-

w4           |

Lnpi1est |   .0047722   .0013724     3.48   0.001     .0020823    .0074622

Lnpi2est |     .00605   .0008998     6.72   0.000     .0042865    .0078136

Lnpi3est |  -.0009457    .000805    -1.17   0.240    -.0025234    .0006321

Lnpi4est |   .0017002   .0014409     1.18   0.238    -.0011238    .0045243

Lnpi5est |  -.0074946   .0004829   -15.52   0.000    -.0084411   -.0065481

Lnpi6est |  -.0040822   .0009762    -4.18   0.000    -.0059955   -.0021688

LnY_riil |  -.0128166   .0006578   -19.48   0.000    -.0141059   -.0115274

Lnage |   .0161683   .0013801    11.72   0.000     .0134634    .0188732

jk |  -.0112451   .0009581   -11.74   0.000     -.013123   -.0093672

edu |  -.0018022    .000969    -1.86   0.063    -.0037014    .0000971

work_i2 |   .0142332   .0013305    10.70   0.000     .0116255    .0168408

work_i3 |   .0224186   .0013364    16.78   0.000     .0197993    .0250379

miskin |   .0196207   .0011231    17.47   0.000     .0174195    .0218219

wil |    -.00715   .0007532    -9.49   0.000    -.0086262   -.0056738

milik |   .0228521   .0018419    12.41   0.000     .0192421    .0264621

IMR4 |    .071912     .00757     9.50   0.000      .057075    .0867489

_cons |   .0491496   .0066729     7.37   0.000      .036071    .0622282

————-+—————————————————————-

w5           |

Lnpi1est |  -.0124223   .0007894   -15.74   0.000    -.0139695   -.0108751

Lnpi2est |  -.0231257   .0007679   -30.12   0.000    -.0246308   -.0216207

Lnpi3est |  -.0159164   .0006158   -25.85   0.000    -.0171234   -.0147095

Lnpi4est |  -.0074946   .0004829   -15.52   0.000    -.0084411   -.0065481

Lnpi5est |   .0661294   .0011268    58.69   0.000     .0639209    .0683378

Lnpi6est |  -.0071703   .0004793   -14.96   0.000    -.0081098   -.0062309

LnY_riil |   .0602257   .0013907    43.31   0.000     .0574999    .0629515

Lnage |  -.0420209   .0034848   -12.06   0.000     -.048851   -.0351908

jk |   .0436777   .0032764    13.33   0.000     .0372561    .0500993

edu |  -.0325499   .0033359    -9.76   0.000    -.0390881   -.0260117

work_i2 |   -.058948   .0040431   -14.58   0.000    -.0668723   -.0510236

work_i3 |  -.0903041   .0039821   -22.68   0.000    -.0981089   -.0824994

miskin |  -.0799647   .0034944   -22.88   0.000    -.0868136   -.0731159

wil |   .0264852    .002586    10.24   0.000     .0214168    .0315536

milik |  -.1102955   .0056338   -19.58   0.000    -.1213375   -.0992535

IMR5 |  -.3758903   .0189404   -19.85   0.000    -.4130129   -.3387677

_cons |   .4018487   .0148289    27.10   0.000     .3727845    .4309129

————-+—————————————————————-

w6           |

Lnpi1est |  -.0171047   .0013405   -12.76   0.000    -.0197321   -.0144772

Lnpi2est |  -.0053999   .0008742    -6.18   0.000    -.0071134   -.0036865

Lnpi3est |  -.0012289   .0007908    -1.55   0.120    -.0027788     .000321

Lnpi4est |  -.0040822   .0009762    -4.18   0.000    -.0059955   -.0021688

Lnpi5est |  -.0071703   .0004793   -14.96   0.000    -.0081098   -.0062309

Lnpi6est |    .034986    .001305    26.81   0.000     .0324282    .0375438

LnY_riil |  -.0199638   .0006324   -31.57   0.000    -.0212033   -.0187242

Lnage |   .0093709   .0013665     6.86   0.000     .0066926    .0120493

jk |  -.0066484    .000949    -7.01   0.000    -.0085084   -.0047885

edu |  -.0056567   .0009593    -5.90   0.000     -.007537   -.0037765

work_i2 |   .0134153   .0013314    10.08   0.000     .0108058    .0160247

work_i3 |   .0179644     .00128    14.03   0.000     .0154557    .0204731

miskin |   .0075043   .0010451     7.18   0.000     .0054559    .0095526

wil |  -.0077561   .0007437   -10.43   0.000    -.0092136   -.0062985

milik |   .0154385   .0017943     8.60   0.000     .0119217    .0189553

IMR6 |  -.0199921   .0078197    -2.56   0.011    -.0353184   -.0046658

_cons |   .1077275   .0065477    16.45   0.000     .0948942    .1205607

——————————————————————————

 

. eststo aids_ipm0

 

. esttab aids_ipm0 using $hasil\aids_ipm0.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\aids_ipm0.rtf)

 

. mean w1 w2 w3 w4 w5 w6

 

Mean estimation                     Number of obs    =   17552

 

————————————————————–

|       Mean   Std. Err.     [95% Conf. Interval]

————-+————————————————

w1 |   .1943217   .0006504      .1930469    .1955966

w2 |   .1330234   .0006673      .1317155    .1343314

w3 |   .1222227   .0004765      .1212887    .1231567

w4 |   .0877691   .0003649      .0870538    .0884844

w5 |   .3638942   .0013151      .3613165     .366472

w6 |   .0987688   .0003544      .0980741    .0994635

————————————————————–

 

. sort urut

 

. tempfile final

 

. save `6kelfinal_ipm0′, replace

file D:\tesis_dewi_aids\running\02_dataproses\\dataanalisis_ipm0.dta saved

 

. save $dataproses\6kelfinal_ipm0.dta, replace

file D:\tesis_dewi_aids\running\02_dataproses\\6kelfinal_ipm0.dta saved

 

.

. *** STEP 5,6,7. RUNNING DATA STATUS IPM=ALL

. use $dataset\analisis_ipmall.dta, clear

 

. order urut r102 r107 r108 food nfood expend exp_cap

 

. keep urut r102 r107 r108 food nfood expend exp_cap

 

. merge 1:1 urut using `wil’

(label r105 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `milik’

(label r1502 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `miskin’

(label r105 already defined)

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `ipm’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `age’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `jk’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `edu’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

. drop _m

 

. merge 1:1 urut using `work’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok1′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok2′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok3′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok4′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok5′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. merge 1:1 urut using `kelompok6′

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

 

 

. merge 1:1 urut using `probit_i’

 

Result                           # of obs.

—————————————–

not matched                             0

matched                            29,467  (_merge==3)

—————————————–

 

. drop _m

 

. rename r102 kab

 

. rename r107 nks

 

. rename r108 urutruta

 

. count

29467

 

. sort urut

 

. order urut food wil milik work work_i1 work_i2 work_i3 miskin ipm age jk edu work kuantitas1bln Lnpi1 LnDev1 kuantitas2bln Lnpi2 LnDev2 kuan

> titas3bln Lnpi3 LnDev3 kuantitas4bln Lnpi4 LnDev4 kuantitas5bln Lnpi5 LnDev5 kuantitas6bln Lnpi6 LnDev6 Yprobit1 Yprobit2 Yprobit3 Yprobit4

> Yprobit5 Yprobit6

 

. rename food Y

 

. gen LnY=ln(Y)

 

. gen Lnage=ln(age)

 

.

. * Data continuos

. sum Y wil milik age jk edu work_i2 work_i3 miskin Lnpi1 Lnpi2 Lnpi3 Lnpi4 Lnpi5 Lnpi6 ipm

 

Variable |       Obs        Mean    Std. Dev.       Min        Max

————-+——————————————————–

Y |     29467     1469416     1031842      66630   1.43e+07

wil |     29467    .5230936    .4994749          0          1

milik |     29467    .9114603     .284083          0          1

age |     29467    51.46493    13.41142         13         97

jk |     29467    .8159297    .3875481          0          1

————-+——————————————————–

edu |     29467     .236943    .4252143          0          1

work_i2 |     29467    .5072793    .4999555          0          1

work_i3 |     29467    .3464214    .4758375          0          1

miskin |     29467    .1038789    .3051086          0          1

Lnpi1 |     29467     8.69186    1.402164          0   9.968073

————-+——————————————————–

Lnpi2 |     29467    9.541782    2.222649          0   12.66033

Lnpi3 |     29467    8.752782    1.545387          0    10.8198

Lnpi4 |     29467    8.654833    1.653118          0   10.66565

Lnpi5 |     29467    9.596152    1.151692          0   14.17219

Lnpi6 |     29467    9.683355    1.371376          0   13.12236

————-+——————————————————–

ipm |     29467    .4043506    .4907743          0          1

 

*5. Hasil Analisis Regresi Ln Deviasi Harga

. *5.1. Kelompok 1. Padi dan umbi-umbian

. reg LnDev1 LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm, f robus

 

Linear regression                                      Number of obs =   29467

F( 10, 29456) =   58.58

Prob > F      =  0.0000

R-squared     =  0.0563

Root MSE      =  1.3345

 

——————————————————————————

|               Robust

LnDev1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .2734896   .0158625    17.24   0.000     .2423985    .3045808

Lnage |   .2646551   .0521237     5.08   0.000     .1624903    .3668199

jk |  -.0218767   .0273426    -0.80   0.424    -.0754693    .0317159

edu |  -.0027985   .0211392    -0.13   0.895    -.0442322    .0386352

work_i2 |   .4126355   .0388367    10.62   0.000     .3365139    .4887571

work_i3 |   .4454607   .0353151    12.61   0.000     .3762416    .5146799

miskin |   .2052237   .0196101    10.47   0.000     .1667869    .2436604

wil |  -.0422446   .0167227    -2.53   0.012    -.0750218   -.0094673

milik |   .6726527   .0491595    13.68   0.000     .5762979    .7690076

ipm |  -.1355081   .0181678    -7.46   0.000    -.1711178   -.0998984

_cons |  -5.968063    .323942   -18.42   0.000    -6.603004   -5.333122

——————————————————————————

 

. predict LnDev1est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.73    0.366085

work_i3 |      2.66    0.375776

LnY |      1.45    0.687499

wil |      1.38    0.726863

Lnage |      1.34    0.747996

edu |      1.29    0.775399

ipm |      1.28    0.783846

jk |      1.24    0.807411

miskin |      1.17    0.851530

milik |      1.13    0.885684

————-+———————-

Mean VIF |      1.57

 

. eststo dev1_ipm0

 

. esttab dev1_ipm0 using $hasil\dev1_ipmall.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev1_ipmall.rtf)

 

. *5.2. Kelompok 2. Ikan, daging, telur dan susu

. reg LnDev2 LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm, f robus

 

Linear regression                                      Number of obs =   29467

F( 10, 29456) =  132.34

Prob > F      =  0.0000

R-squared     =  0.0807

Root MSE      =   2.085

 

——————————————————————————

|               Robust

LnDev2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .8374161   .0269244    31.10   0.000      .784643    .8901891

Lnage |  -.0011334   .0690709    -0.02   0.987    -.1365155    .1342487

jk |  -.1201818   .0407854    -2.95   0.003    -.2001231   -.0402405

edu |  -.0103093   .0300991    -0.34   0.732    -.0693048    .0486863

work_i2 |   .4302274   .0543957     7.91   0.000     .3236094    .5368454

work_i3 |   .5493706    .052255    10.51   0.000     .4469486    .6517927

miskin |   .1712056   .0489115     3.50   0.000     .0753368    .2670744

wil |  -.0794182   .0270455    -2.94   0.003    -.1324285   -.0264078

milik |   .7705084   .0615352    12.52   0.000     .6498966    .8911202

ipm |  -.2536371    .027807    -9.12   0.000      -.30814   -.1991341

_cons |  -13.09545   .4633432   -28.26   0.000    -14.00363   -12.18728

——————————————————————————

 

. predict LnDev2est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.73    0.366085

work_i3 |      2.66    0.375776

LnY |      1.45    0.687499

wil |      1.38    0.726863

Lnage |      1.34    0.747996

edu |      1.29    0.775399

ipm |      1.28    0.783846

jk |      1.24    0.807411

miskin |      1.17    0.851530

milik |      1.13    0.885684

————-+———————-

Mean VIF |      1.57

 

. eststo dev2_ipm0

 

. esttab dev2_ipm0 using $hasil\dev2_ipmall.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev2_ipmall.rtf)

 

. *5.3. Kelompok 3. Sayur dan buah-buahan

. reg LnDev3 LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm, f robus

 

Linear regression                                      Number of obs =   29467

F( 10, 29456) =   74.43

Prob > F      =  0.0000

R-squared     =  0.0643

Root MSE      =  1.4496

 

——————————————————————————

|               Robust

LnDev3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .4219463   .0192079    21.97   0.000     .3842979    .4595947

Lnage |   .2482276   .0537899     4.61   0.000     .1427969    .3536582

jk |   -.130603   .0283738    -4.60   0.000    -.1862169   -.0749891

edu |   .0430247   .0223602     1.92   0.054    -.0008023    .0868516

work_i2 |   .4728878   .0415109    11.39   0.000     .3915247     .554251

work_i3 |   .5112831   .0383299    13.34   0.000     .4361548    .5864113

miskin |   .2577565   .0241869    10.66   0.000     .2103492    .3051638

wil |  -.0820457   .0185636    -4.42   0.000    -.1184312   -.0456602

milik |   .6119333   .0494864    12.37   0.000     .5149377    .7089288

ipm |   -.186322   .0204619    -9.11   0.000    -.2264282   -.1462159

_cons |  -7.925441   .3641819   -21.76   0.000    -8.639254   -7.211628

——————————————————————————

 

. predict LnDev3est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.73    0.366085

work_i3 |      2.66    0.375776

LnY |      1.45    0.687499

wil |      1.38    0.726863

Lnage |      1.34    0.747996

edu |      1.29    0.775399

ipm |      1.28    0.783846

jk |      1.24    0.807411

miskin |      1.17    0.851530

milik |      1.13    0.885684

————-+———————-

Mean VIF |      1.57

 

. eststo dev3_ipm0

 

. esttab dev3_ipm0 using $hasil\dev3_ipmall.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev3_ipmall.rtf)

 

. *5.4. Kelompok 4. Kacang dan minyak

. reg LnDev4 LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm, f robus

 

Linear regression                                      Number of obs =   29467

F( 10, 29456) =   78.04

Prob > F      =  0.0000

R-squared     =  0.0705

Root MSE      =  1.5552

 

——————————————————————————

|               Robust

LnDev4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |    .389983   .0194735    20.03   0.000     .3518141    .4281519

Lnage |   .3617921   .0575782     6.28   0.000     .2489362     .474648

jk |  -.0575129   .0308189    -1.87   0.062    -.1179193    .0028936

edu |  -.0049827   .0247339    -0.20   0.840    -.0534624    .0434969

work_i2 |   .5369997   .0439417    12.22   0.000     .4508721    .6231273

work_i3 |   .5657735   .0399501    14.16   0.000     .4874695    .6440775

miskin |   .2989559   .0238148    12.55   0.000     .2522779    .3456339

wil |  -.0791309   .0193187    -4.10   0.000    -.1169964   -.0412654

milik |    .813482   .0549828    14.80   0.000     .7057132    .9212507

ipm |  -.2193138   .0216248   -10.14   0.000    -.2616994   -.1769282

_cons |  -8.217871   .3777282   -21.76   0.000    -8.958235   -7.477507

——————————————————————————

 

. predict LnDev4est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.73    0.366085

work_i3 |      2.66    0.375776

LnY |      1.45    0.687499

wil |      1.38    0.726863

Lnage |      1.34    0.747996

edu |      1.29    0.775399

ipm |      1.28    0.783846

jk |      1.24    0.807411

miskin |      1.17    0.851530

milik |      1.13    0.885684

————-+———————-

Mean VIF |      1.57

 

. eststo dev4_ipm0

 

. esttab dev4_ipm0 using $hasil\dev4_ipmall.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev4_ipmall.rtf)

 

. *5.5. Kelompok 5. Makanan jadi dan rokok

. reg LnDev5 LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm, f robus

 

Linear regression                                      Number of obs =   29467

F( 10, 29456) =  111.65

Prob > F      =  0.0000

R-squared     =  0.0978

Root MSE      =  1.0947

 

——————————————————————————

|               Robust

LnDev5 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .4711139   .0161321    29.20   0.000     .4394943    .5027335

Lnage |   -.061846   .0258136    -2.40   0.017    -.1124419   -.0112502

jk |   .1718252    .022277     7.71   0.000     .1281612    .2154891

edu |  -.1558377   .0129858   -12.00   0.000    -.1812905   -.1303848

work_i2 |  -.0145981   .0240381    -0.61   0.544    -.0617138    .0325176

work_i3 |   .0441229    .028188     1.57   0.118    -.0111268    .0993726

miskin |  -.2316451   .0335429    -6.91   0.000    -.2973908   -.1658995

wil |   .0628705   .0152287     4.13   0.000     .0330217    .0927193

milik |  -.0340717   .0215015    -1.58   0.113    -.0762156    .0080722

ipm |  -.0527064   .0143236    -3.68   0.000    -.0807813   -.0246314

_cons |  -6.691004   .2244344   -29.81   0.000    -7.130905   -6.251103

——————————————————————————

 

. predict LnDev5est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.73    0.366085

work_i3 |      2.66    0.375776

LnY |      1.45    0.687499

wil |      1.38    0.726863

Lnage |      1.34    0.747996

edu |      1.29    0.775399

ipm |      1.28    0.783846

jk |      1.24    0.807411

miskin |      1.17    0.851530

milik |      1.13    0.885684

————-+———————-

Mean VIF |      1.57

 

. eststo dev5_ipm0

 

. esttab dev5_ipm0 using $hasil\dev5_ipmall.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev5_ipmall.rtf)

 

. *5.6. Kelompok 6. Bahan Pangan Lainnya

. reg LnDev6 LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm, f robus

 

Linear regression                                      Number of obs =   29467

F( 10, 29456) =   55.71

Prob > F      =  0.0000

R-squared     =  0.0583

Root MSE      =  1.2994

 

——————————————————————————

|               Robust

LnDev6 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

LnY |   .2940292   .0162211    18.13   0.000      .262235    .3258233

Lnage |   .2726646   .0543183     5.02   0.000     .1661982    .3791309

jk |  -.0366814     .02683    -1.37   0.172    -.0892694    .0159065

edu |   .0120998   .0207579     0.58   0.560    -.0285867    .0527862

work_i2 |   .4591862   .0399475    11.49   0.000     .3808874     .537485

work_i3 |   .4378393   .0363997    12.03   0.000     .3664942    .5091844

miskin |   .1411975   .0208292     6.78   0.000     .1003713    .1820236

wil |  -.0701621   .0162078    -4.33   0.000    -.1019302    -.038394

milik |   .6160905     .04886    12.61   0.000     .5203226    .7118583

ipm |  -.1219645   .0179393    -6.80   0.000    -.1571262   -.0868027

_cons |  -6.215616   .3459693   -17.97   0.000    -6.893731     -5.5375

——————————————————————————

 

. predict LnDev6est

(option xb assumed; fitted values)

 

. estat vif

 

Variable |       VIF       1/VIF

————-+———————-

work_i2 |      2.73    0.366085

work_i3 |      2.66    0.375776

LnY |      1.45    0.687499

wil |      1.38    0.726863

Lnage |      1.34    0.747996

edu |      1.29    0.775399

ipm |      1.28    0.783846

jk |      1.24    0.807411

miskin |      1.17    0.851530

milik |      1.13    0.885684

————-+———————-

Mean VIF |      1.57

 

. eststo dev6_ipm0

 

. esttab dev6_ipm0 using $hasil\dev6_ipmall.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\dev6_ipmall.rtf)

 

. *5.1.1. Generate Lnpiest

. gen Lnpi1est=(Lnpi1-LnDev1est) if Yprobit1==1

(729 missing values generated)

 

. replace Lnpi1est=(Lnpi1mean- LnDev1est) if Yprobit1==0

(729 real changes made)

 

. gen Lnpi2est=(Lnpi2-LnDev2est) if Yprobit2==1

(1467 missing values generated)

 

. replace Lnpi2est=(Lnpi2mean- LnDev2est) if Yprobit2==0

(1467 real changes made)

 

. gen Lnpi3est=(Lnpi3-LnDev3est) if Yprobit3==1

(826 missing values generated)

 

. replace Lnpi3est=(Lnpi3mean- LnDev3est) if Yprobit3==0

(826 real changes made)

 

. gen Lnpi4est=(Lnpi4-LnDev4est) if Yprobit4==1

(1009 missing values generated)

 

. replace Lnpi4est=(Lnpi4mean- LnDev4est) if Yprobit4==0

(1009 real changes made)

 

. gen Lnpi5est=(Lnpi5-LnDev5est) if Yprobit5==1

(251 missing values generated)

 

. replace Lnpi5est=(Lnpi5mean- LnDev5est) if Yprobit5==0

(251 real changes made)

 

. gen Lnpi6est=(Lnpi6-LnDev6est) if Yprobit6==1

(551 missing values generated)

 

. replace Lnpi6est=(Lnpi6mean- LnDev6est) if Yprobit6==0

(551 real changes made)

 

. label variable Lnpi1est “Ln unit value kel 1 hasil estimasi”

 

. label variable Lnpi2est “Ln unit value kel 2 hasil estimasi”

 

. label variable Lnpi3est “Ln unit value kel 3 hasil estimasi”

 

. label variable Lnpi4est “Ln unit value kel 4 hasil estimasi”

 

. label variable Lnpi5est “Ln unit value kel 5 hasil estimasi”

 

. label variable Lnpi6est “Ln unit value kel 6 hasil estimasi”

 

. sort urut

 

. *5.1.2. Generate Variabel Budget Share kelompok komoditi ke-i(wi)

. gen w1 = (harga1bln/Y)

 

. gen w2 = (harga2bln/Y)

 

. gen w3 = (harga3bln/Y)

 

. gen w4 = (harga4bln/Y)

 

. gen w5 = (harga5bln/Y)

 

. gen w6 = (harga6bln/Y)

 

. replace w1=0 if w1==.

(0 real changes made)

 

. replace w2=0 if w2==.

(0 real changes made)

 

. replace w3=0 if w3==.

(0 real changes made)

 

. replace w4=0 if w4==.

(0 real changes made)

 

. replace w5=0 if w5==.

(0 real changes made)

 

. replace w6=0 if w6==.

(0 real changes made)

 

. label variable w1 “budget share kel 1”

 

. label variable w2 “budget share kel 2”

 

. label variable w3 “budget share kel 3”

 

. label variable w4 “budget share kel 4”

 

. label variable w5 “budget share kel 5”

 

. label variable w6 “budget share kel 6”

 

. count

29467

. sort urut

 

. *6. Analisis Regresi Probit

. *6.1. Kelompok 1. Padi dan umbi-umbian

. probit Yprobit1 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

Iteration 0:   log likelihood = -3416.7351

Iteration 1:   log likelihood = -2634.4541

Iteration 2:   log likelihood = -2540.3643

Iteration 3:   log likelihood = -2539.3115

Iteration 4:   log likelihood = -2539.3107

Iteration 5:   log likelihood = -2539.3107

 

Probit regression                                 Number of obs   =      29467

LR chi2(16)     =    1754.85

Prob > chi2     =     0.0000

Log likelihood = -2539.3107                       Pseudo R2       =     0.2568

 

——————————————————————————

Yprobit1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   1.874968   .0985173    19.03   0.000     1.681877    2.068058

Lnpi2est |  -.1485259   .0533728    -2.78   0.005    -.2531347   -.0439172

Lnpi3est |  -.1952748   .0511066    -3.82   0.000    -.2954419   -.0951076

Lnpi4est |    .683031   .0800343     8.53   0.000     .5261666    .8398954

Lnpi5est |  -.1641866   .0253441    -6.48   0.000    -.2138602    -.114513

Lnpi6est |  -.6191497   .0678139    -9.13   0.000    -.7520625    -.486237

LnY |   .6453105    .045714    14.12   0.000     .5557128    .7349083

Lnage |    .704143   .0730941     9.63   0.000     .5608812    .8474047

jk |   .0113594   .0473252     0.24   0.810    -.0813963    .1041151

edu |  -.1749984   .0486134    -3.60   0.000    -.2702789   -.0797179

work_i2 |   1.198299   .0682361    17.56   0.000     1.064559     1.33204

work_i3 |    1.71309   .0858159    19.96   0.000     1.544894    1.881286

miskin |   1.456952   .1068704    13.63   0.000      1.24749    1.666414

wil |  -.0860937   .0459314    -1.87   0.061    -.1761176    .0039302

milik |   1.913787   .0911771    20.99   0.000     1.735084    2.092491

ipm |  -.5203371   .0484672   -10.74   0.000    -.6153312   -.4253431

_cons |  -24.33987   1.591992   -15.29   0.000    -27.46012   -21.21963

——————————————————————————

 

. outreg2 using $hasil\probit1_ipmall,dec(3) replace

dir : seeout

 

. predict probitx1, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      29467

Model VCE    : OIM

 

Expression   : Pr(Yprobit1), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0836388   .0047325    17.67   0.000     .0743632    .0929144

Lnpi2est |  -.0066255   .0023854    -2.78   0.005    -.0113008   -.0019502

Lnpi3est |  -.0087108   .0022872    -3.81   0.000    -.0131936   -.0042281

Lnpi4est |   .0304687   .0036438     8.36   0.000     .0233269    .0376105

Lnpi5est |  -.0073241   .0011411    -6.42   0.000    -.0095605   -.0050876

Lnpi6est |  -.0276191   .0030874    -8.95   0.000    -.0336703   -.0215679

LnY |   .0287861   .0021421    13.44   0.000     .0245876    .0329846

Lnage |   .0314105   .0033368     9.41   0.000     .0248705    .0379505

jk |   .0005067   .0021111     0.24   0.810    -.0036309    .0046444

edu |  -.0078064   .0021761    -3.59   0.000    -.0120715   -.0035412

work_i2 |   .0534539   .0032619    16.39   0.000     .0470608     .059847

work_i3 |   .0764177    .004211    18.15   0.000     .0681642    .0846712

miskin |   .0649919    .004996    13.01   0.000     .0551999    .0747839

wil |  -.0038405   .0020506    -1.87   0.061    -.0078595    .0001785

milik |   .0853705   .0044788    19.06   0.000     .0765922    .0941487

ipm |  -.0232113   .0022254   -10.43   0.000    -.0275731   -.0188495

——————————————————————————

 

. outreg2 using $hasil\probit1_mfx_ipmall.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit1_mfx_ipmall.xls

dir : seeout

 

. *6.2. Kelompok 2. Ikan, daging, telur dan susu

. probit Yprobit2 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

Iteration 0:   log likelihood = -5830.9371

Iteration 1:   log likelihood = -4898.5221

Iteration 2:   log likelihood = -4834.7147

Iteration 3:   log likelihood = -4834.5443

Iteration 4:   log likelihood = -4834.5443

 

Probit regression                                 Number of obs   =      29467

LR chi2(16)     =    1992.79

Prob > chi2     =     0.0000

Log likelihood = -4834.5443                       Pseudo R2       =     0.1709

 

——————————————————————————

Yprobit2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .2603205    .076465     3.40   0.001     .1104518    .4101892

Lnpi2est |   .1225939   .0423446     2.90   0.004        .0396    .2055879

Lnpi3est |  -.2604077   .0368323    -7.07   0.000    -.3325977   -.1882177

Lnpi4est |   .8037152   .0595317    13.50   0.000     .6870352    .9203951

Lnpi5est |  -.1825634   .0167678   -10.89   0.000    -.2154277   -.1496992

Lnpi6est |  -.1694573    .051861    -3.27   0.001    -.2711029   -.0678117

LnY |   .8993488   .0369523    24.34   0.000     .8269237    .9717739

Lnage |   .1999728    .057191     3.50   0.000     .0878804    .3120652

jk |  -.1010566   .0359817    -2.81   0.005    -.1715795   -.0305337

edu |   -.107182   .0392057    -2.73   0.006    -.1840237   -.0303402

work_i2 |   .6945683   .0518564    13.39   0.000     .5929316    .7962051

work_i3 |   .9161826   .0583611    15.70   0.000     .8017969    1.030568

miskin |   .5072057   .0510623     9.93   0.000     .4071255    .6072859

wil |  -.0676157   .0330206    -2.05   0.041    -.1323348   -.0028966

milik |   1.199832   .0705714    17.00   0.000     1.061515    1.338149

ipm |  -.4022152   .0362216   -11.10   0.000    -.4732083   -.3312222

_cons |  -18.11447   1.231549   -14.71   0.000    -20.52827   -15.70068

——————————————————————————

 

. outreg2 using $hasil\probit2_ipmall,dec(3) replace

dir : seeout

 

. predict probitx2, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      29467

Model VCE    : OIM

 

Expression   : Pr(Yprobit2), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0225145   .0066217     3.40   0.001     .0095362    .0354928

Lnpi2est |   .0106028   .0036635     2.89   0.004     .0034225    .0177832

Lnpi3est |   -.022522   .0031971    -7.04   0.000    -.0287882   -.0162558

Lnpi4est |   .0695113   .0052252    13.30   0.000     .0592702    .0797525

Lnpi5est |  -.0157895   .0014583   -10.83   0.000    -.0186477   -.0129312

Lnpi6est |  -.0146559     .00449    -3.26   0.001    -.0234562   -.0058556

LnY |   .0777824   .0033528    23.20   0.000     .0712111    .0843538

Lnage |   .0172951   .0049546     3.49   0.000     .0075844    .0270059

jk |  -.0087401   .0031146    -2.81   0.005    -.0148446   -.0026357

edu |  -.0092699   .0033932    -2.73   0.006    -.0159204   -.0026194

work_i2 |   .0600715   .0045487    13.21   0.000     .0511562    .0689868

work_i3 |   .0792383    .005146    15.40   0.000     .0691523    .0893244

miskin |    .043867   .0044571     9.84   0.000     .0351312    .0526027

wil |  -.0058479   .0028568    -2.05   0.041    -.0114471   -.0002487

milik |   .1037705   .0062358    16.64   0.000     .0915485    .1159925

ipm |  -.0347866   .0031651   -10.99   0.000      -.04099   -.0285831

——————————————————————————

 

. outreg2 using $hasil\probit2_mfx_ipmall.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit2_mfx_ipmall.xls

dir : seeout

 

. *6.3. Kelompok 3. Sayur dan buah-buahan

. probit Yprobit3 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

Iteration 0:   log likelihood = -3766.7938

Iteration 1:   log likelihood = -3077.6606

Iteration 2:   log likelihood = -3009.2309

Iteration 3:   log likelihood = -3008.3366

Iteration 4:   log likelihood = -3008.3352

Iteration 5:   log likelihood = -3008.3352

 

Probit regression                                 Number of obs   =      29467

LR chi2(16)     =    1516.92

Prob > chi2     =     0.0000

Log likelihood = -3008.3352                       Pseudo R2       =     0.2014

 

——————————————————————————

Yprobit3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .4343074    .092681     4.69   0.000     .2526559    .6159588

Lnpi2est |  -.0561379   .0500084    -1.12   0.262    -.1541527    .0418768

Lnpi3est |   .2152101   .0486758     4.42   0.000     .1198073    .3106129

Lnpi4est |   .5145855   .0725428     7.09   0.000     .3724041    .6567669

Lnpi5est |  -.1724608   .0221011    -7.80   0.000    -.2157781   -.1291434

Lnpi6est |  -.3460375   .0625277    -5.53   0.000    -.4685895   -.2234855

LnY |    .757495     .04317    17.55   0.000     .6728834    .8421065

Lnage |   .4760656   .0673199     7.07   0.000     .3441209    .6080102

jk |  -.2102065   .0450876    -4.66   0.000    -.2985766   -.1218365

edu |  -.0369126   .0459701    -0.80   0.422    -.1270124    .0531871

work_i2 |   .8801391   .0615156    14.31   0.000     .7595707    1.000708

work_i3 |   1.217326   .0742389    16.40   0.000     1.071821    1.362832

miskin |   1.224493   .0904467    13.54   0.000     1.047221    1.401765

wil |  -.1313827   .0417822    -3.14   0.002    -.2132742   -.0494911

milik |   1.186664   .0824243    14.40   0.000     1.025116    1.348213

ipm |  -.4893519   .0442284   -11.06   0.000     -.576038   -.4026658

_cons |   -16.7563   1.451763   -11.54   0.000     -19.6017   -13.91089

——————————————————————————

 

. outreg2 using $hasil\probit3_ipmall,dec(3) replace

dir : seeout

 

. predict probitx3, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      29467

Model VCE    : OIM

 

Expression   : Pr(Yprobit3), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0229703   .0049255     4.66   0.000     .0133165     .032624

Lnpi2est |  -.0029691   .0026459    -1.12   0.262    -.0081549    .0022167

Lnpi3est |   .0113823   .0025855     4.40   0.000     .0063149    .0164498

Lnpi4est |   .0272161   .0038839     7.01   0.000     .0196039    .0348284

Lnpi5est |  -.0091214   .0011817    -7.72   0.000    -.0114375   -.0068053

Lnpi6est |  -.0183017   .0033282    -5.50   0.000    -.0248249   -.0117786

LnY |   .0400635   .0024435    16.40   0.000     .0352743    .0448527

Lnage |   .0251788   .0036038     6.99   0.000     .0181155    .0322422

jk |  -.0111177   .0023968    -4.64   0.000    -.0158154     -.00642

edu |  -.0019523   .0024317    -0.80   0.422    -.0067183    .0028137

work_i2 |   .0465501   .0033985    13.70   0.000     .0398891     .053211

work_i3 |   .0643837   .0041731    15.43   0.000     .0562045    .0725629

miskin |   .0647628   .0049875    12.98   0.000     .0549874    .0745382

wil |  -.0069488   .0022148    -3.14   0.002    -.0112898   -.0026077

milik |    .062762   .0045562    13.78   0.000     .0538321     .071692

ipm |  -.0258816   .0024045   -10.76   0.000    -.0305944   -.0211687

——————————————————————————

 

. outreg2 using $hasil\probit3_mfx_ipmall.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit3_mfx_ipmall.xls

dir : seeout

 

. *6.4. Kelompok 4. Kacang dan minyak

. probit Yprobit4 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

Iteration 0:   log likelihood = -4396.2045

Iteration 1:   log likelihood = -3437.4343

Iteration 2:   log likelihood = -3336.2482

Iteration 3:   log likelihood = -3335.3737

Iteration 4:   log likelihood = -3335.3735

 

Probit regression                                 Number of obs   =      29467

LR chi2(16)     =    2121.66

Prob > chi2     =     0.0000

Log likelihood = -3335.3735                       Pseudo R2       =     0.2413

 

——————————————————————————

Yprobit4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .6175769   .0890893     6.93   0.000     .4429651    .7921888

Lnpi2est |  -.2149277   .0459212    -4.68   0.000    -.3049316   -.1249237

Lnpi3est |   -.203541   .0447588    -4.55   0.000    -.2912666   -.1158153

Lnpi4est |   1.361947   .0716196    19.02   0.000     1.221576    1.502319

Lnpi5est |  -.1665404   .0217729    -7.65   0.000    -.2092145   -.1238663

Lnpi6est |  -.5637804   .0598304    -9.42   0.000    -.6810458    -.446515

LnY |   .6676909   .0396887    16.82   0.000     .5899025    .7454792

Lnage |   .6444859   .0656343     9.82   0.000      .515845    .7731268

jk |  -.0547956   .0425069    -1.29   0.197    -.1381076    .0285164

edu |  -.1138253   .0429505    -2.65   0.008    -.1980067   -.0296439

work_i2 |   1.048974   .0606146    17.31   0.000     .9301717    1.167777

work_i3 |   1.417153    .073751    19.22   0.000     1.272604    1.561703

miskin |   1.375939   .0918281    14.98   0.000     1.195959    1.555919

wil |  -.1185023    .040781    -2.91   0.004    -.1984316   -.0385731

milik |    1.56315   .0804748    19.42   0.000     1.405422    1.720877

ipm |  -.5762732   .0430331   -13.39   0.000    -.6606165     -.49193

_cons |  -18.58929   1.397206   -13.30   0.000    -21.32777   -15.85082

——————————————————————————

 

. outreg2 using $hasil\probit4_ipmall,dec(3) replace

dir : seeout

 

. predict probitx4, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      29467

Model VCE    : OIM

 

Expression   : Pr(Yprobit4), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0367645   .0053435     6.88   0.000     .0262915    .0472376

Lnpi2est |  -.0127947   .0027429    -4.66   0.000    -.0181707   -.0074187

Lnpi3est |  -.0121169   .0026723    -4.53   0.000    -.0173545   -.0068792

Lnpi4est |   .0810771   .0044824    18.09   0.000     .0722918    .0898625

Lnpi5est |  -.0099142   .0013052    -7.60   0.000    -.0124724    -.007356

Lnpi6est |   -.033562   .0036087    -9.30   0.000    -.0406349   -.0264891

LnY |   .0397478   .0024627    16.14   0.000      .034921    .0445746

Lnage |   .0383664   .0039676     9.67   0.000       .03059    .0461428

jk |   -.003262   .0025311    -1.29   0.197    -.0082229    .0016989

edu |  -.0067761     .00256    -2.65   0.008    -.0117935   -.0017586

work_i2 |   .0624457   .0037614    16.60   0.000     .0550736    .0698179

work_i3 |   .0843636   .0046452    18.16   0.000     .0752592     .093468

miskin |   .0819101   .0056565    14.48   0.000     .0708235    .0929966

wil |  -.0070545   .0024311    -2.90   0.004    -.0118194   -.0022895

milik |   .0930548   .0050391    18.47   0.000     .0831782    .1029313

ipm |  -.0343057   .0026311   -13.04   0.000    -.0394626   -.0291489

——————————————————————————

. outreg2 using $hasil\probit4_mfx_ipmall.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit4_mfx_ipmall.xls

dir : seeout

 

. *6.5. Kelompok 5. Makanan jadi dan rokok

. probit Yprobit5 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

Iteration 0:   log likelihood = -1446.0868

Iteration 1:   log likelihood = -1013.1559

Iteration 2:   log likelihood = -903.82529

Iteration 3:   log likelihood = -896.70835

Iteration 4:   log likelihood = -896.65604

Iteration 5:   log likelihood = -896.65603

 

Probit regression                                 Number of obs   =      29467

LR chi2(16)     =    1098.86

Prob > chi2     =     0.0000

Log likelihood = -896.65603                       Pseudo R2       =     0.3799

 

——————————————————————————

Yprobit5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .1475913   .1678395     0.88   0.379    -.1813682    .4765507

Lnpi2est |   -.240545   .0846419    -2.84   0.004      -.40644     -.07465

Lnpi3est |    .002689   .0774045     0.03   0.972    -.1490211    .1543991

Lnpi4est |   -.268602   .1291954    -2.08   0.038    -.5218202   -.0153837

Lnpi5est |  -.4325578   .0263204   -16.43   0.000    -.4841448   -.3809708

Lnpi6est |  -.2559215    .113355    -2.26   0.024    -.4780933   -.0337498

LnY |    .739434    .083533     8.85   0.000     .5757123    .9031556

Lnage |  -.4606865   .1507029    -3.06   0.002    -.7560587   -.1653142

jk |  -.0142159   .0759713    -0.19   0.852    -.1631169    .1346851

edu |   -.247787   .1213794    -2.04   0.041    -.4856862   -.0098877

work_i2 |   -.224223   .1249106    -1.80   0.073    -.4690432    .0205973

work_i3 |  -.3825479    .126021    -3.04   0.002    -.6295444   -.1355513

miskin |  -.0938002   .0957208    -0.98   0.327    -.2814094    .0938091

wil |   .1373589   .0745812     1.84   0.066    -.0088175    .2835353

milik |  -.5095257   .1890989    -2.69   0.007    -.8801526   -.1388987

ipm |   .1198471   .0843486     1.42   0.155    -.0454732    .2851674

_cons |   5.893036   2.858135     2.06   0.039     .2911944    11.49488

——————————————————————————

Note: 0 failures and 1 success completely determined.

 

. outreg2 using $hasil\probit5_ipmall,dec(3) replace

dir : seeout

 

. predict probitx5, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      29467

Model VCE    : OIM

Expression   : Pr(Yprobit5), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0022797   .0025938     0.88   0.379     -.002804    .0073634

Lnpi2est |  -.0037155   .0013158    -2.82   0.005    -.0062943   -.0011366

Lnpi3est |   .0000415   .0011956     0.03   0.972    -.0023018    .0023849

Lnpi4est |  -.0041488   .0020025    -2.07   0.038    -.0080736   -.0002241

Lnpi5est |  -.0066813   .0004835   -13.82   0.000    -.0076289   -.0057337

Lnpi6est |   -.003953   .0017595    -2.25   0.025    -.0074015   -.0005045

LnY |   .0114214   .0013842     8.25   0.000     .0087083    .0141344

Lnage |  -.0071158   .0023491    -3.03   0.002      -.01172   -.0025116

jk |  -.0002196   .0011733    -0.19   0.852    -.0025193    .0020801

edu |  -.0038273   .0018816    -2.03   0.042    -.0075153   -.0001394

work_i2 |  -.0034634   .0019345    -1.79   0.073    -.0072549    .0003282

work_i3 |  -.0059089   .0019628    -3.01   0.003    -.0097559   -.0020619

miskin |  -.0014488   .0014788    -0.98   0.327    -.0043473    .0014496

wil |   .0021217   .0011559     1.84   0.066    -.0001438    .0043872

milik |  -.0078702   .0029374    -2.68   0.007    -.0136273    -.002113

ipm |   .0018512   .0013054     1.42   0.156    -.0007074    .0044098

——————————————————————————

. outreg2 using $hasil\probit5_mfx_ipmall.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit5_mfx_ipmall.xls

dir : seeout

 

. *6.6. Kelompok 6. Bahan Pangan Lainnya

. probit Yprobit6 Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

Iteration 0:   log likelihood = -2738.4056

Iteration 1:   log likelihood =  -2161.214

Iteration 2:   log likelihood = -2088.9079

Iteration 3:   log likelihood = -2088.0082

Iteration 4:   log likelihood = -2088.0081

 

Probit regression                                 Number of obs   =      29467

LR chi2(16)     =    1300.80

Prob > chi2     =     0.0000

Log likelihood = -2088.0081                       Pseudo R2       =     0.2375

 

——————————————————————————

Yprobit6 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .5977588   .1084482     5.51   0.000     .3852042    .8103134

Lnpi2est |  -.1916064   .0564835    -3.39   0.001     -.302312   -.0809009

Lnpi3est |  -.1271806   .0557359    -2.28   0.022     -.236421   -.0179402

Lnpi4est |   .8078377   .0873852     9.24   0.000     .6365658    .9791095

Lnpi5est |  -.1119529   .0271315    -4.13   0.000    -.1651297   -.0587761

Lnpi6est |   .4018066   .0829498     4.84   0.000      .239228    .5643853

LnY |   .6768545   .0498861    13.57   0.000     .5790795    .7746296

Lnage |   .7889734    .078142    10.10   0.000      .635818    .9421288

jk |  -.0090513   .0511566    -0.18   0.860    -.1093164    .0912138

edu |  -.1098692    .053971    -2.04   0.042    -.2156504   -.0040879

work_i2 |   1.334659   .0748014    17.84   0.000     1.188051    1.481267

work_i3 |   1.542947   .0892233    17.29   0.000     1.368073    1.717821

miskin |   1.256061   .1031897    12.17   0.000     1.053813    1.458309

wil |   -.141064   .0504401    -2.80   0.005    -.2399248   -.0422032

milik |   1.842924   .0989722    18.62   0.000     1.648942    2.036906

ipm |  -.5119617   .0531122    -9.64   0.000    -.6160597   -.4078637

_cons |  -25.41392   1.758751   -14.45   0.000    -28.86101   -21.96683

——————————————————————————

 

. outreg2 using $hasil\probit6_ipmall,dec(3) replace

dir : seeout

 

. predict probitx6, xb

 

. margins, dydx(*) post

 

Average marginal effects                          Number of obs   =      29467

Model VCE    : OIM

 

Expression   : Pr(Yprobit6), predict()

dy/dx w.r.t. : Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY Lnage jk edu work_i2 work_i3 miskin wil milik ipm

 

——————————————————————————

|            Delta-method

|      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

Lnpi1est |   .0216674   .0039748     5.45   0.000     .0138768    .0294579

Lnpi2est |  -.0069453   .0020562    -3.38   0.001    -.0109754   -.0029152

Lnpi3est |    -.00461   .0020238    -2.28   0.023    -.0085765   -.0006435

Lnpi4est |   .0292822   .0032719     8.95   0.000     .0228694    .0356951

Lnpi5est |   -.004058   .0009893    -4.10   0.000     -.005997    -.002119

Lnpi6est |   .0145646   .0030352     4.80   0.000     .0086156    .0205135

LnY |   .0245344   .0019405    12.64   0.000     .0207311    .0283378

Lnage |   .0285985   .0029428     9.72   0.000     .0228308    .0343662

jk |  -.0003281   .0018544    -0.18   0.860    -.0039626    .0033064

edu |  -.0039825   .0019596    -2.03   0.042    -.0078232   -.0001418

work_i2 |   .0483783   .0030259    15.99   0.000     .0424477    .0543089

work_i3 |   .0559283   .0036094    15.49   0.000     .0488539    .0630026

miskin |   .0455293   .0039589    11.50   0.000     .0377701    .0532885

wil |  -.0051132   .0018342    -2.79   0.005    -.0087081   -.0015184

milik |   .0668017   .0040418    16.53   0.000     .0588799    .0747236

ipm |  -.0185574   .0019957    -9.30   0.000    -.0224689    -.014646

——————————————————————————

 

. outreg2 using $hasil\probit6_mfx_ipmall.xls,ctitle(margins)dec(3) replace

D:\tesis_dewi_aids\running\04_hasil\\probit6_mfx_ipmall.xls

dir : seeout

 

. *6.1.1. Generate Invers Mills RAtio (IMR)

. gen IMR1 = (normalden( probitx1)/normprob( probitx1))

 

. gen IMR2 = (normalden( probitx2)/normprob( probitx2))

 

. gen IMR3 = (normalden( probitx3)/normprob( probitx3))

 

. gen IMR4 = (normalden( probitx4)/normprob( probitx4))

 

. gen IMR5 = (normalden( probitx5)/normprob( probitx5))

 

. gen IMR6 = (normalden( probitx6)/normprob( probitx6))

 

. label variable IMR1 “Inverse Mills Ratio Kel 1”

 

. label variable IMR2 “Inverse Mills Ratio Kel 2”

 

. label variable IMR3 “Inverse Mills Ratio Kel 3”

 

. label variable IMR4 “Inverse Mills Ratio Kel 4”

 

. label variable IMR5 “Inverse Mills Ratio Kel 5”

 

. label variable IMR6 “Inverse Mills Ratio Kel 6”

 

.

. *6.1.2. Generate Indeks Harga Stone

. gen LnIndexStone=((w1*Lnpi1est)+(w2*Lnpi2est)+(w3*Lnpi3est)+(w4*Lnpi4est)+(w5*Lnpi5est)+(w6*Lnpi6est))

 

. gen LnY_riil = (LnY- LnIndexStone)

 

. label variable LnIndexStone “Ln Indeks harga Stone”

 

. label variable LnY_riil “Ln Pengeluaran riil”

 

. *7. Analisis Regresi Dengan Menerapkan Restriksi Permintaan (LA_AIDS Model)

. **  Menerapkan Restriksi Permintaan yang terdiri atas :

. *         1. Simetri

. *         2. Adding Up

. *         3. Homogeneity

.

. constraint define 1 [w1]Lnpi2est=[w2]Lnpi1est

 

. constraint define 2 [w1]Lnpi3est=[w3]Lnpi1est

 

. constraint define 3 [w1]Lnpi4est=[w4]Lnpi1est

 

. constraint define 4 [w1]Lnpi5est=[w5]Lnpi1est

 

. constraint define 5 [w1]Lnpi6est=[w6]Lnpi1est

 

. constraint define 6 [w2]Lnpi3est=[w3]Lnpi2est

 

. constraint define 7 [w2]Lnpi4est=[w4]Lnpi2est

 

. constraint define 8 [w2]Lnpi5est=[w5]Lnpi2est

 

. constraint define 9 [w2]Lnpi6est=[w6]Lnpi2est

 

. constraint define 10 [w3]Lnpi4est=[w4]Lnpi3est

 

. constraint define 11 [w3]Lnpi5est=[w5]Lnpi3est

 

. constraint define 12 [w3]Lnpi6est=[w6]Lnpi3est

 

. constraint define 13 [w4]Lnpi5est=[w5]Lnpi4est

 

. constraint define 14 [w4]Lnpi6est=[w6]Lnpi4est

 

. constraint define 15 [w5]Lnpi6est=[w6]Lnpi5est

 

. constraint define 16 [w1]Lnpi1est=-[w1]Lnpi2est-[w1]Lnpi3est-[w1]Lnpi4est-[w1]Lnpi5est-[w1]Lnpi6est

 

. constraint define 17 [w2]Lnpi2est=-[w2]Lnpi1est-[w2]Lnpi3est-[w2]Lnpi4est-[w2]Lnpi5est-[w2]Lnpi6est

 

. constraint define 18 [w3]Lnpi3est=-[w3]Lnpi1est-[w3]Lnpi2est-[w3]Lnpi4est-[w3]Lnpi5est-[w3]Lnpi6est

 

. constraint define 19 [w4]Lnpi4est=-[w4]Lnpi1est-[w4]Lnpi2est-[w4]Lnpi3est-[w4]Lnpi5est-[w4]Lnpi6est

 

. constraint define 20 [w5]Lnpi5est=-[w5]Lnpi1est-[w5]Lnpi2est-[w5]Lnpi3est-[w5]Lnpi4est-[w5]Lnpi6est

 

. constraint define 21 [w6]Lnpi6est=-[w6]Lnpi1est-[w6]Lnpi2est-[w6]Lnpi3est-[w6]Lnpi4est-[w6]Lnpi5est

 

. constraint 22 [w1]LnY_riil=-[w2]LnY_riil-[w3]LnY_riil-[w4]LnY_riil-[w5]LnY_riil-[w6]LnY_riil

 

. constraint 23 [w1]_cons+[w2]_cons+[w3]_cons+[w4]_cons+[w5]_cons+[w6]_cons=1

 

. set matsize 800

 

. reg3 (w1 = Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est ///

> LnY_riil Lnage jk edu work_i2 work_i3 miskin wil milik ipm IMR1) (w2=Lnpi1est ///

> Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk edu work_i2 ///

> work_i3 miskin wil milik ipm IMR2) (w3=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est ///

> Lnpi6est LnY_riil Lnage jk edu work_i2 work_i3 miskin wil milik ipm IMR3) ///

> (w4=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk ///

> edu work_i2 work_i3 miskin wil milik ipm IMR4) (w5=Lnpi1est Lnpi2est Lnpi3est ///

> Lnpi4est Lnpi5est Lnpi6est LnY_riil Lnage jk edu work_i2 work_i3 miskin wil ///

> milik ipm IMR5) (w6=Lnpi1est Lnpi2est Lnpi3est Lnpi4est Lnpi5est Lnpi6est LnY_riil ///

> Lnage jk edu work_i2 work_i3 miskin wil milik ipm IMR6) , constraints(23 22 21 20 ///

> 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1) ireg3 nodfk sure corr(independent)

 

Iteration 1:   tolerance =  .06463027

Iteration 2:   tolerance =  .00047082

Iteration 3:   tolerance =  .00001028

Iteration 4:   tolerance =  3.818e-07

 

Seemingly unrelated regression, iterated

———————————————————————-

Equation          Obs  Parms        RMSE    “R-sq”       chi2        P

———————————————————————-

w1              29467     16    .0692927    0.3439   15838.69   0.0000

w2              29467     16    .0849147    0.2135    8502.62   0.0000

w3              29467     16    .0612913    0.0936    3233.39   0.0000

w4              29467     16    .0432029    0.2030    7353.16   0.0000

w5              29467     16    .1620161    0.2286   15538.98   0.0000

w6              29467     16    .0421643    0.1909    7135.11   0.0000

———————————————————————-

 

( 1)  [w1]_cons + [w2]_cons + [w3]_cons + [w4]_cons + [w5]_cons + [w6]_cons = 1

( 2)  [w1]LnY_riil + [w2]LnY_riil + [w3]LnY_riil + [w4]LnY_riil + [w5]LnY_riil + [w6]LnY_riil = 0

( 3)  [w6]Lnpi1est + [w6]Lnpi2est + [w6]Lnpi3est + [w6]Lnpi4est + [w6]Lnpi5est + [w6]Lnpi6est = 0

( 4)  [w5]Lnpi1est + [w5]Lnpi2est + [w5]Lnpi3est + [w5]Lnpi4est + [w5]Lnpi5est + [w5]Lnpi6est = 0

( 5)  [w4]Lnpi1est + [w4]Lnpi2est + [w4]Lnpi3est + [w4]Lnpi4est + [w4]Lnpi5est + [w4]Lnpi6est = 0

( 6)  [w3]Lnpi1est + [w3]Lnpi2est + [w3]Lnpi3est + [w3]Lnpi4est + [w3]Lnpi5est + [w3]Lnpi6est = 0

( 7)  [w2]Lnpi1est + [w2]Lnpi2est + [w2]Lnpi3est + [w2]Lnpi4est + [w2]Lnpi5est + [w2]Lnpi6est = 0

( 8)  [w1]Lnpi1est + [w1]Lnpi2est + [w1]Lnpi3est + [w1]Lnpi4est + [w1]Lnpi5est + [w1]Lnpi6est = 0

( 9)  [w5]Lnpi6est – [w6]Lnpi5est = 0

(10)  [w4]Lnpi6est – [w6]Lnpi4est = 0

(11)  [w4]Lnpi5est – [w5]Lnpi4est = 0

(12)  [w3]Lnpi6est – [w6]Lnpi3est = 0

(13)  [w3]Lnpi5est – [w5]Lnpi3est = 0

(14)  [w3]Lnpi4est – [w4]Lnpi3est = 0

(15)  [w2]Lnpi6est – [w6]Lnpi2est = 0

(16)  [w2]Lnpi5est – [w5]Lnpi2est = 0

(17)  [w2]Lnpi4est – [w4]Lnpi2est = 0

(18)  [w2]Lnpi3est – [w3]Lnpi2est = 0

(19)  [w1]Lnpi6est – [w6]Lnpi1est = 0

(20)  [w1]Lnpi5est – [w5]Lnpi1est = 0

(21)  [w1]Lnpi4est – [w4]Lnpi1est = 0

(22)  [w1]Lnpi3est – [w3]Lnpi1est = 0

(23)  [w1]Lnpi2est – [w2]Lnpi1est = 0

 

——————————————————————————

|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

————-+—————————————————————-

w1           |

Lnpi1est |   .0533718    .001971    27.08   0.000     .0495086    .0572349

Lnpi2est |  -.0202526   .0010509   -19.27   0.000    -.0223123   -.0181928

Lnpi3est |  -.0110546   .0009091   -12.16   0.000    -.0128365   -.0092728

Lnpi4est |   .0081694   .0010261     7.96   0.000     .0061583    .0101805

Lnpi5est |  -.0167262   .0005917   -28.27   0.000     -.017886   -.0155664

Lnpi6est |  -.0135078   .0009841   -13.73   0.000    -.0154367   -.0115789

LnY_riil |  -.0325927   .0007034   -46.33   0.000    -.0339714    -.031214

Lnage |   .0316123   .0017094    18.49   0.000      .028262    .0349626

jk |   .0073078   .0011594     6.30   0.000     .0050354    .0095801

edu |  -.0192433   .0010675   -18.03   0.000    -.0213355   -.0171511

work_i2 |   .0241198   .0014709    16.40   0.000      .021237    .0270026

work_i3 |   .0402764    .001542    26.12   0.000     .0372542    .0432986

miskin |   .1003143   .0014537    69.00   0.000      .097465    .1031635

wil |  -.0132495   .0009548   -13.88   0.000    -.0151209   -.0113781

milik |   .0464272   .0018178    25.54   0.000     .0428645      .04999

ipm |  -.0269613   .0009417   -28.63   0.000    -.0288071   -.0251156

IMR1 |   .0383913   .0056881     6.75   0.000     .0272429    .0495397

_cons |   .1789209   .0073353    24.39   0.000     .1645441    .1932978

————-+—————————————————————-

w2           |

Lnpi1est |  -.0202526   .0010509   -19.27   0.000    -.0223123   -.0181928

Lnpi2est |   .0493409   .0011704    42.16   0.000     .0470469    .0516349

Lnpi3est |   .0044755    .000758     5.90   0.000     .0029899    .0059611

Lnpi4est |   -.002588   .0007078    -3.66   0.000    -.0039753   -.0012006

Lnpi5est |  -.0208338   .0006275   -33.20   0.000    -.0220637    -.019604

Lnpi6est |   -.010142   .0006757   -15.01   0.000    -.0114663   -.0088177

LnY_riil |   .0093104   .0009274    10.04   0.000     .0074927    .0111282

Lnage |  -.0041485   .0019966    -2.08   0.038    -.0080617   -.0002353

jk |   -.019359   .0014105   -13.72   0.000    -.0221235   -.0165944

edu |   .0414495   .0013055    31.75   0.000     .0388907    .0440082

work_i2 |  -.0125152   .0017322    -7.22   0.000    -.0159103   -.0091201

work_i3 |  -.0151952    .001828    -8.31   0.000    -.0187781   -.0116123

miskin |  -.0221697   .0017217   -12.88   0.000    -.0255442   -.0187951

wil |   .0027281   .0011693     2.33   0.020     .0004363    .0050198

milik |   .0073716   .0021018     3.51   0.000     .0032522    .0114911

ipm |   .0039466   .0011496     3.43   0.001     .0016933    .0061998

IMR2 |   -.237127   .0067996   -34.87   0.000     -.250454   -.2237999

_cons |   .1094634   .0088052    12.43   0.000     .0922055    .1267214

————-+—————————————————————-

w3           |

Lnpi1est |  -.0110546   .0009091   -12.16   0.000    -.0128365   -.0092728

Lnpi2est |   .0044755    .000758     5.90   0.000     .0029899    .0059611

Lnpi3est |   .0252886   .0009191    27.51   0.000     .0234871      .02709

Lnpi4est |  -.0016517   .0006234    -2.65   0.008    -.0028735   -.0004299

Lnpi5est |  -.0152874     .00049   -31.20   0.000    -.0162477    -.014327

Lnpi6est |  -.0017704   .0006019    -2.94   0.003      -.00295   -.0005908

LnY_riil |  -.0044436   .0006409    -6.93   0.000    -.0056997   -.0031876

Lnage |   .0319873   .0014993    21.34   0.000     .0290488    .0349259

jk |  -.0212364   .0010285   -20.65   0.000    -.0232523   -.0192206

edu |   .0120684   .0009458    12.76   0.000     .0102147    .0139221

work_i2 |   .0122475   .0013498     9.07   0.000     .0096019     .014893

work_i3 |   .0179236   .0014205    12.62   0.000     .0151395    .0207077

miskin |   .0019086   .0013217     1.44   0.149     -.000682    .0044991

wil |  -.0023192   .0008484    -2.73   0.006    -.0039821   -.0006564

milik |   .0233479   .0015966    14.62   0.000     .0202186    .0264772

ipm |   -.006647   .0008493    -7.83   0.000    -.0083115   -.0049824

IMR3 |  -.0005135   .0067756    -0.08   0.940    -.0137934    .0127665

_cons |   .0045722   .0070254     0.65   0.515    -.0091973    .0183418

————-+—————————————————————-

 

 

w4           |

Lnpi1est |   .0081694   .0010261     7.96   0.000     .0061583    .0101805

Lnpi2est |   -.002588   .0007078    -3.66   0.000    -.0039753   -.0012006

Lnpi3est |  -.0016517   .0006234    -2.65   0.008    -.0028735   -.0004299

Lnpi4est |   .0074281   .0011377     6.53   0.000     .0051983    .0096579

Lnpi5est |  -.0092962   .0003728   -24.94   0.000    -.0100269   -.0085656

Lnpi6est |  -.0020616   .0007337    -2.81   0.005    -.0034996   -.0006236

LnY_riil |  -.0190184   .0004603   -41.31   0.000    -.0199206   -.0181161

Lnage |   .0259787   .0011211    23.17   0.000     .0237814    .0281759

jk |  -.0057238   .0007243    -7.90   0.000    -.0071433   -.0043042

edu |   -.005682   .0006673    -8.52   0.000    -.0069898   -.0043742

work_i2 |   .0151742   .0009652    15.72   0.000     .0132825    .0170659

work_i3 |   .0215967   .0009995    21.61   0.000     .0196377    .0235556

miskin |   .0237163   .0009445    25.11   0.000      .021865    .0255675

wil |  -.0052444    .000597    -8.78   0.000    -.0064145   -.0040742

milik |   .0259848   .0011934    21.77   0.000     .0236457    .0283239

ipm |  -.0052916   .0006051    -8.75   0.000    -.0064775   -.0041056

IMR4 |   .0104313   .0035749     2.92   0.004     .0034246    .0174381

_cons |   .0426513   .0050487     8.45   0.000      .032756    .0525465

————-+—————————————————————-

w5           |

Lnpi1est |  -.0167262   .0005917   -28.27   0.000     -.017886   -.0155664

Lnpi2est |  -.0208338   .0006275   -33.20   0.000    -.0220637    -.019604

Lnpi3est |  -.0152874     .00049   -31.20   0.000    -.0162477    -.014327

Lnpi4est |  -.0092962   .0003728   -24.94   0.000    -.0100269   -.0085656

Lnpi5est |   .0697378   .0008858    78.73   0.000     .0680018    .0714739

Lnpi6est |  -.0075942   .0003641   -20.86   0.000    -.0083077   -.0068807

LnY_riil |   .0686064   .0010369    66.17   0.000     .0665741    .0706387

Lnage |  -.0796997   .0028191   -28.27   0.000    -.0852249   -.0741744

jk |   .0243924    .002652     9.20   0.000     .0191946    .0295902

edu |  -.0246875   .0024495   -10.08   0.000    -.0294883   -.0198866

work_i2 |  -.0739726   .0030508   -24.25   0.000    -.0799521   -.0679931

work_i3 |  -.1045369   .0032425   -32.24   0.000    -.1108921   -.0981818

miskin |  -.0890748   .0032517   -27.39   0.000    -.0954481   -.0827016

wil |   .0230586   .0022233    10.37   0.000     .0187011    .0274162

milik |  -.1503574   .0036409   -41.30   0.000    -.1574934   -.1432214

ipm |    .043298   .0021595    20.05   0.000     .0390655    .0475306

IMR5 |  -.2941196   .0174915   -16.81   0.000    -.3284023   -.2598368

_cons |   .5739849   .0115673    49.62   0.000     .5513135    .5966564

————-+—————————————————————-

w6           |

Lnpi1est |  -.0135078   .0009841   -13.73   0.000    -.0154367   -.0115789

Lnpi2est |   -.010142   .0006757   -15.01   0.000    -.0114663   -.0088177

Lnpi3est |  -.0017704   .0006019    -2.94   0.003      -.00295   -.0005908

Lnpi4est |  -.0020616   .0007337    -2.81   0.005    -.0034996   -.0006236

Lnpi5est |  -.0075942   .0003641   -20.86   0.000    -.0083077   -.0068807

Lnpi6est |    .035076   .0009356    37.49   0.000     .0332422    .0369098

LnY_riil |  -.0218622   .0004448   -49.15   0.000     -.022734   -.0209903

Lnage |   .0155685   .0010904    14.28   0.000     .0134313    .0177057

jk |  -.0031828   .0007081    -4.49   0.000    -.0045707    -.001795

edu |   -.006464   .0006515    -9.92   0.000    -.0077409   -.0051872

work_i2 |   .0167033   .0009481    17.62   0.000     .0148452    .0185615

work_i3 |   .0210853    .000961    21.94   0.000     .0192018    .0229688

miskin |   .0109091   .0008896    12.26   0.000     .0091655    .0126526

wil |  -.0067093   .0005828   -11.51   0.000    -.0078515    -.005567

milik |   .0166412    .001135    14.66   0.000     .0144167    .0188658

ipm |  -.0074346    .000575   -12.93   0.000    -.0085616   -.0063076

IMR6 |  -.0115622   .0047484    -2.43   0.015     -.020869   -.0022554

_cons |   .0904072   .0049174    18.39   0.000     .0807693    .1000451

——————————————————————————

. eststo aids_ipm0

 

. esttab aids_ipm0 using $hasil\aids_ipm_all.rtf, replace se star(* 0.1 ** 0.05 *** 0.01)

(output written to D:\tesis_dewi_aids\running\04_hasil\aids_ipm_all.rtf)

 

 

 

. mean w1 w2 w3 w4 w5 w6

 

Mean estimation                     Number of obs    =   29467

 

————————————————————–

|       Mean   Std. Err.     [95% Conf. Interval]

————-+————————————————

w1 |   .1733008   .0004983      .1723241    .1742776

w2 |   .1421908   .0005578      .1410975    .1432841

w3 |   .1217613    .000375      .1210262    .1224964

w4 |   .0824013   .0002819      .0818487    .0829539

w5 |   .3885966   .0010746      .3864902    .3907029

w6 |   .0917491   .0002731      .0912139    .0922844

————————————————————–

. sort urut

 

. tempfile final

 

. save `6kelfinal_ipm_all’, replace

file D:\tesis_dewi_aids\running\03_dataset\\analisis_ipmall.dta saved

 

. save $dataproses\6kelfinal_ipm_all.dta, replace

file D:\tesis_dewi_aids\running\02_dataproses\\6kelfinal_ipm_all.dta saved

 

end of do-file

. exit, clear

Dias Satria
Dias Satria
Dias Satria, Research field :Economic development, international trade, Banking and small/medium enterprise Email. dias.satria@gmail.com Mobile Phone. +62 81 333 828 319 Office Phone. +62 341 551 396

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