Annika,
Expanding Rodrigo's comments about recent theory developments, I think you
should have a look at the paper:
"Understanding Bias in Nonlinear Panel Models: Some Recent Developments"
(Arellano and Hahn), Invited Lecture, Econometric Society World Congress,
London, August 2005.
which you can obtain from:
http://www.cemfi.es/~arellano/#WPapers
Hope this helps,
Marcos
Quoting "Rodrigo A. Alfaro" <[email protected]>:
> The question is valid for a linear model, in that case it is fine to say:
> autocorrelation or heteroskedasticity with endogenous regressors. The
> algorithm used in -ivreg2- for dealing with the unspherical errors only
> affects the standard errors. In the case of the model that you are asking it
> is hard to think that this is a feasible solution. Adding sample selection
> will make the things even more complicated, are you thinking in a two-step
> model or maximum-likelihood?
>
> My suggestion will be similar to Maarten's proposal. If you are doing this
> for a empirical paper, it is better to use -xtlogit- or -xtprobit- (both are
> random effects)... you will spend a lot of time checking that the model is
> right under different quadratures. Moreover, for a reasonable number of
> cross sectional units and a powerful computer you can estimate the logit and
> probit model adding dummies for the cross sectional units. In the case of
> logit, -clogit- could help you in this matter. There are corrections for the
> probit+fe model, but W. Greene considers that the raw-estimation is fine.
>
> Now, if your live depends on this estimation and you have the time to spend
> on it you can write down the loglikelihood assciated with your problem. In
> that case, you define the endogeneity (linear or even not linear) of your
> covariate, the behaviro of the unobserved component (fixed or random
> effects) and also the sample selection rule. Finally, you could impose a
> reasonable structure for the "error" such as AR(1) with heteroskedasticity.
> All of these can be estimated by -ml- routines that you have to code on your
> own. With a reasonable load of 10-hours per day, 6 days a week you will not
> spend less than 6 months. For the case of fixed effects you need to improve
> the algorithm to make feasible the estimation, Greene proposes an algorithm
> to improve the estimationof MLE with fixed effects avoiding to add the
> dummies. Moreover, you need to fix your code to allow unbalanced panels (if
> you need it). You need to prove things like consistency and asymptotic
> normality to believe in your estimator... and probably those are different
> for large-n fixed-T and large-n large-T asymptotics. In conclusion, it is
> not simple... and this could be the main reason why there is not such a
> package!! A lazy and risky alternative is to search on the web if that
> medicine was written for someone else, I doubt that there is something even
> close that you are looking for. Maybe something smaller in features and
> available for Matlab, Gauss of Fortran.
>
> Rodrigo.
>
>
>
> ----- Original Message -----
> From: "Maarten Buis" <[email protected]>
> To: <[email protected]>
> Sent: Monday, June 11, 2007 9:45 AM
> Subject: st: RE: Nonlinear panel data models and their efficient estimation
>
>
> > 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
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> >
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
*
* For searches and help try:
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