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st: 2SLS with probit in the first stage regression


From   Renuka Metcalfe <rm18203@yahoo.co.uk>
To   statalist <statalist@hsphsun2.harvard.edu>
Subject   st: 2SLS with probit in the first stage regression
Date   Fri, 15 Feb 2008 13:04:41 +0000 (GMT)

Thank you, Kit. I should have mentioned also that my
original equation which has one of the RHS regressors
as potentially endogenous is a random effects GLS
model. Also I should have pointed out I am using
cross-section data. Would your kind answer still
apply?

I appreciate your reply greatly.
Thanks
Renuka

Please read the Baum-Schaffer-Stillman paper, SJ 2007
(preprint available from my website below) on this
subject. You can estimate the equation with an
endogenous dummy consistently with IV, and in our
-ivreg2- you can use the endog() option to test
whether that variable must be considered endogenous.
You can use -xtivreg2- to perform a fixed effect
model, as -xtivreg2- has the same options available as
does -ivreg2- in terms of endogeneity tests.


Kit Baum, Boston College Economics and DIW Berlin
http://ideas.repec.org/e/pba1.html
An Introduction to Modern Econometrics Using Stata:
http://www.stata-press.com/books/imeus.html

On Feb 15, 2008, at 2:33 AM, Renuka wrote:



I would like to find out if training which is one of
my RHS variable is an endogenous variable. The
original regression was a pay equation as follows:

.xtreg y training meanworkplacetraining x2 x3 x4

I used xtreg as the data are grouped across workplaces
and considered to be a less biased OLS estimator.
Training is a binary variable, dummy=1 if they have
had training and 0 otherwise and the data is
cross-section data. I left out the meanworkplace
training for now. I want to deal with one endogenous
variable at a time.

I did:

.probit training x2 x3 x4 z

where z = my instrument

I then did:

predict ghat

I then issued:

.ivreg pay x2 (training = ghat) x3 x4

Part of what I got is:

Number of obs =   14321

 F( 58, 14262) =  115.93
 Prob > F      =  0.0000
 R-squared     =  0.3104
 Adj R-squared =  0.3076
 Root MSE      =  .49362

         Coef.   Std. Err.   t  P>|t|
-
-------------+----------------------------------------
training |.2904799 .116244 2.50  0.012

Can I take interpret that the training variable is not
endogenous? I would be grateful if anyone would tell
me if I have done it correctly to find out if my
training variable is endogenous and if it is
endogenous. If I have done it incorrectly what would
be the correct way to go about it.


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