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st: How to derive Wooldridge and Orme approaches in xtprobit module ?

From   "Karabulut, Yigitcan" <>
To   "" <>
Subject   st: How to derive Wooldridge and Orme approaches in xtprobit module ?
Date   Fri, 19 Mar 2010 13:46:12 +0100

Dear Statlist,

I am trying to estimate a dynamic random effects probit model that has the following reduced form:

Y_it = beta*x_it + gamma*y_t-1+alpha_i +u_it (

where y_it  is a binary variable, alpha_i represents the individual specific unobserved heterogeneity, u_it is the unobserved error, x_it is a vector of regressors)

In order to account for unobserved heterogeneity and initial conditions problems, I have employed the Heckman approach (Heckman, 1981) using the program module redprob by Prof. Mark Stewart. However, also as stressed in the literature (e.g. Arulampan and Steward, 2009 or Capellari and Jenkins, 2008), the computation of Heckman estimator takes so long (for instance,  Capellari and Jenkins ( 2008) notes that their estimation took about 15 hours). 

Since the other two approaches; Orme (1996) and Wooldridge (2002) do also provide similar results as the Heckman estimator (Arulampan and Steward, 2009), I am willing to employ these approaches since the estimation is less "expensive" (also as a robustness check). 

I read that I can derive the Orme and Wooldridge approaches from the program module xtprobit, however, I could not figure out how.  I was wondering if anyone knows how to derive these approaches in xtprobit module in Stata?




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