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Re: st: Re: Re: selection bias with bivariate probit
Unless I've understood the specification incorrectly, there are at least
2 ways to do it in Stata.
1. Ignore the binary nature of both outcome and treatment and use linear
IV (-ivregress-, -ivreg2-).
2. Assume bivariate normality (which -cmp- does as well) and use the
bivariate probit model (-biprobit- ).
cheers,
Partha
Martin Weiss wrote:
<>
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" <[email protected]>
To: <[email protected]>
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" <[email protected]>
To: <[email protected]>
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
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--
Partha Deb
Professor of Economics
Hunter College
ph: (212) 772-5435
fax: (212) 772-5398
http://urban.hunter.cuny.edu/~deb/
Emancipate yourselves from mental slavery
None but ourselves can free our minds.
- Bob Marley
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