So you want to estimate a fixed/random effects model with
heteroscedasticity, serial correlation, and sample selection
correction and endogenous variables? As you can imagine this
is not going to be easy. My advise would be to first think
very very very hard about whether you really need it. So
think about the question you are trying to answer. Do this
with somebody else who hasn't worked on your problem before.
When somebody comes into my office with the question of how
to ``bootstrap a multilevel sample selection MCMC propensity
weighted semi parametric model'' the outcome has always been
that there is a much simpler way of answering the research
question (sometimes as easy as comparing 4 means, which
economist tend to call a difference in difference estimator
but I think terminology like that just hides the simplicity
of procedures like that (but maybe that is intentional)).
Hope this helps (and isn't too pretentious),
Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
-----Original Message-----
From: [email protected] [mailto:[email protected]]On Behalf Of Annika Meng
Sent: maandag 11 juni 2007 14:51
To: [email protected]
Subject: st: Nonlinear panel data models and their efficient estimation
Dear Statalist users,
I would like to estimate a binary panel data model with fixed and random effects with "xtlogit" / "xtprobit". However, I found no information on how to correct my estimated coefficients for heteroskedasticity and serial correlation. In addition, I have to deal with endogenous regressors and sample selection.
The Stata command "ivreg2" is the command for these problems concerning linear endogenous variables.
Does anyone know if there is a nonlinear equivalent or another way to correct the estimated standard errors?
Thank you for your replies.
Annika Meng
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