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Re: st: RE: estimating a heckman sample selection model with panel data
I am afraid I disagree. I don't think that including the IMR's this
way will correct the problem.
Panel sample selection models that work with the same spirit as in the
Heckman selectivity correction (of 1979) were developed by Gert Ridder
(1990), Nijman and Verbeek (1992) and Vella and Verbeek(1994).
The precise formulas that you need to include for a correction term
are no equal to the IMR.
I reccommend you look at the paper by Francis Vella "Estimating Models
with Sample Selection Bias: A survey" in the Journal of Human
Resources (1998), XXXIII, 1. This is a great survey paper of the topic
for the applied researcher.
These models impose strong assumptions and are computationally
burdensome. The alternatives mentioned by Alejandro are good ones.
Actually the Wooldrige 95 model uses IMR's, but not out of a random
effects probit, but of period specific probits. The paper that I
mentioned by Vella includes explanations for these models too.
Alternatively you can also look at Wooldrige's own graduate textbook,
where he discusses his own method in a more compact and clear way .
On 6/15/05, Alejandro Lopez-Feldman <email@example.com> wrote:
> It seems to me that your procedure might work well, altough it will only
> work for random effects and not for fixed effects since xtprobit is only
> valid for random effects. Also, as you do with the regular heckman, I think
> that you will have to obtain the errors in a way that accounts for the fact
> that you are including a predicted value (instead of an observed one) in
> the second stage.
> There are two alternatives that I know of to get fixed effects (or
> something close to it) this are the references:
> Kyriazidou, E. (1997). Estimation of a Panel Data Sample Selection Model.
> Econometrica. 65 (6), pp. 1335-1364.
> Wooldridge, J. (1995). Selection Corrections for Panel Data Models under
> Conditional Mean Independence Assumptions. Journal of Econometrics. 68, pp.
> > ----- Original Message -----
> > From: <Colin.Vance@dlr.de>
> > To: <firstname.lastname@example.org>
> > Sent: Wednesday, June 15, 2005 4:08 AM
> > Subject: st: estimating a heckman sample selection model with panel data
> >> Hello Everyone,
> >> I have a panel of data and would like to estimate a Heckman sample
> >> selection model. This can apparently be done with gllamm, though I'm
> >> still struggling a bit with the code and interpretation (and may have
> >> questions on that later).
> >> My current question is whether the following simple alternative could
> > be
> >> availed: 1. estimate the random effects probit part of the model using
> >> xtprobit 2. calculate the inverse Mills ratio from the results, which
> >> equals normden(linear_pred)/norm(linear_pred) 3. Include the Mills as
> > an
> >> explanatory variable in the second stage regression to control for
> >> selectivity bias. In the second stage regression, one would have to
> >> decide between a fixed effects and random effects specification. And I
> >> believe one would also want to use the robust option.
> >> This approach seems simple enough, but I'm hesitant because I've never
> >> seen it done in the literature. Instead, most articles on the panle
> >> Heckman are fairly equation dense, and few of the findings have made
> >> their way into statistical software (with a few exceptions like
> > gllamm).
> >> If anyone has any insights as to whether the suggested alternative is
> >> defensible, please pass them along.
> >> Many thanks,
> >> Colin
> >> Colin Vance, Ph.D.
> >> German Aerospace Center
> >> Institute of Transport Research
> >> Rutherfordstrasse 2
> >> 12489 Berlin
> >> Germany
> >> tel: +49 30 67055147
> >> fax: +49 30 67055202
> >> email: email@example.com
> >> *
> >> * For searches and help try:
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> >> * http://www.ats.ucla.edu/stat/stata/
> Alejandro Lopez-Feldman
> Ph.D. Candidate
> Agricultural and Resource Economics
> University of California-Davis
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