Statalist The Stata Listserver


[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

st: Re: RE: Nonlinear panel data models and their efficient estimation


From   "Rodrigo A. Alfaro" <[email protected]>
To   <[email protected]>
Subject   st: Re: RE: Nonlinear panel data models and their efficient estimation
Date   Mon, 11 Jun 2007 11:01:17 -0400

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/



© Copyright 1996–2024 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index