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st: multivariate probit models with unobserved selection


From   Zhi Su <su.zh@husky.neu.edu>
To   statalist <statalist@hsphsun2.harvard.edu>
Subject   st: multivariate probit models with unobserved selection
Date   Sun, 15 May 2011 22:55:30 -0400

I have four latent variables
y*_1=x_1*b_1+e_1, y_1=1 if y*_1>0
y*_2=x_2*b_2+e_2, y_2=1 if y*_2>0
y*_3=x_3*b_3+e_3, y_3=1 if y*_3>0
y*_4=x_4*b_4+e_4, y_4=1 if y*_4>0
where e_1, e_2, e_3, e_4 are error terms distributed as multivariate
normal with variance-covariance matrix V.
If a generalized term e is correlated with y*_1, y*_2, y*_3, and y*_4,
how to estimate this generalized term?
Or a term similar to the inverse Mills ratio in the first stage
equation of Heckman 2-stage selection model.
-- 
Zhi Su
348 Holmes Hall
Northeastern University
360 Huntington Avenue
Boston, MA 02115
Office:1-617-373-2316
email:su.zh@husky.neu.edu
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