Ekonometrika

March 3, 2014

Interpretation PROBIT model

x1, x2 and x3 are more likely to have a contract with middlemen, and x4, x5 and x6 are less likely to have a contract. Marginal effect (ME): x1, x2 and x3 are N% more likely to have a contract with middlemen. The predicted probabilities are limited between 0 and 1. Probit are estimated using the ML method. an increase in x increases/decreases the likelihood that y=1 an increase in x makes the outcome of 1 more likely. We interpret the sign of the coefficient but not the magnitude. The magnitude can’t be interpreted using the coefficient because different model […]
March 3, 2014

Do File STATA – Probit Model

Here is the important syntax for probit analysis. Thanks to Ani Kutchova [youtube]https://www.youtube.com/watch?v=wU1DVbpD9SY&feature=player_embedded[/youtube] global ylist Contract_e1 global xlist Age d_notgoingschool No_of_child Marital_status social_group own_boat human_spec frequency Riskbehavior search_costs nego_costs monitor_costs trust market_info connected_lending Andon_fisher d_javanese d_bugis uncertainty describe $ylist $xlist summarize $ylist $xlist list $ylist $xlist in 1/10 tab $ylist reg $ylist $xlist probit $ylist $xlist quietly probit $ylist $xlist margins, dydx(*) atmeans margins, dydx(*) quietly probit $ylist $xlist predict pprobit, pr quietly probit $ylist $xlist estat classification
March 2, 2014

Probit Model in YOUTUBE

[youtube]http://www.youtube.com/watch?v=1cFYlMjEz-c[/youtube]   [youtube]http://www.youtube.com/watch?v=66Mn9GAWrAE[/youtube]   [youtube]http://www.youtube.com/watch?v=wU1DVbpD9SY[/youtube]
February 14, 2014

Data dalam statistik

Source: http://www.ats.ucla.edu/stat/mult_pkg/whatstat/nominal_ordinal_interval.htm What is the difference between categorical, ordinal and interval variables? In talking about variables, sometimes you hear variables being described as categorical (or sometimes nominal), or ordinal, or interval.  Below we will define these terms and explain why they are important. Categorical A categorical variable (sometimes called a nominal variable) is one that has two or more categories, but there is no intrinsic ordering to the categories.  For example, gender is a categorical variable having two categories (male and female) and there is no intrinsic ordering to the categories.  Hair color is also a categorical variable having a number of […]