st: Re: Re: selection bias with bivariate probit

["Martin Weiss" <martin.weiss1@gmx.de>]

<>

Sam privately asked for more details on my post. I think that the 
user-written package -cmp- could be of use to him, so I recommend he install 
it by typing -ssc install cmp- within Stata (while being connected to the 
internet) and subsequently take a look at the help file by typing - help 
cmp- . The file is quite comprehensive, with clickable examples for most 
constellations of data -cmp- is designed to handle. I hope that Sam succeeds 
and that other listers can give additional guidance to him...

HTH
Martin
_______________________
----- Original Message ----- 
From: "Martin Weiss" <martin.weiss1@gmx.de>
To: <statalist@hsphsun2.harvard.edu>
Sent: Wednesday, May 20, 2009 11:18 PM
Subject: st: Re: selection bias with bivariate probit


> <>
>
> Try -ssc d cmp-
>
> HTH
> Martin
> _______________________
> ----- Original Message ----- 
> From: "Sam Lee" <sunghanl@uic.edu>
> To: <statalist@hsphsun2.harvard.edu>
> Sent: Wednesday, May 20, 2009 11:15 PM
> Subject: st: selection bias with bivariate probit
>
>
> > Hello all,
> >
> > I'm looking for a procedure to control selection bias, but a bit special 
> > case that I don't know whether STATA supports this.
> > Outcome regression: dichotomous dependent variable (high earnings or not) 
> > y = beta * x + gamma * z (college or not)
> > Selection regression: dichotomous choice dependent variable (college 
> > degree or not) z = alpha * w
> >
> > Classic Heckman procedure deals with the situation where a selection 
> > model with a dummy dependent variable (work or not) and an outcome model 
> > with a continuous dependent variable (wage) with truncated (only observed 
> > if chosen).
> > My challenges to use a Heckman procedure are two folds: (1) a dummy 
> > variable (high wage or not instead of continuous wage) in an outcome 
> > model (2) outcome observations not truncated (we observe earnings for 
> > both college degree and non-college degree) - so this is more of a 
> > treatment model instead of a selection model.
> > STATA has "heckprob" dealing with the first problem and "treatreg" 
> > dealing with the second problem. But so far I couldn't find any stata 
> > function or ado file dealing with both extensions.
> >
> > As far as I know, we can include both mills ratio of selected and 
> > not-selected in the outcome model from the selection model.
> > Then I'll have a consistent coefficient estimate for gamma. But I don't 
> > know how to correct std dev of gamma in the outcome probit model (I guess 
> > since it is difficult to deal with covariance matrix, I prefer a standard 
> > procedure supporting this correction).
> >
> > Your help will be greatly appreciated.
> >
> > Sincerely,
> >
> > Sam Lee
> >
> >
> > Sam Lee
> >
> > Department of Accounting
> >
> > College of Business Administration
> > University of Illinois (RM# 2302)
> > 601 S. Morgan St, Chicago Il 60607
> >
> > Ph:312.413.2131 Fax:312.996.4520
> >
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/help.cgi?search
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/

*
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