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Re: st: 3 Problems in Panel Data Analysis


From   "Fardad Zand" <fardad.zand@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: 3 Problems in Panel Data Analysis
Date   Tue, 14 Oct 2008 10:41:49 +0200

Hi Nils,

Indeed, thank you so much for your insightful, careful and complete
answers. Just to clarify some remaining issues, I post some
complementary questions, if you can  answer them. I really appreciate
your time.

1- concerning point 1: do you know an other test in place of Hausman
test? Is there any formal way to test for the conditions of RE (i.e.
correlation between unobserved heterogeneity and the variables of
interest)? How can tell something justifiable about this correlation?
Only based on theoretical arguments or is there any test whatsoever
for this purpose?

As an alternative you suggest using IV. But you suggest that the IV
should not be correltated with the outcome. I think you meant the
other way around. Right? Nevertheless, in my case there is no
meaningful, relevant IV available; so, this approach is out of
question.

2- to be honest, I didn't exactly get your point. Sorry for my limited
econometric knowledge. What I know is that if my error terms are
heteroskedasticit, then the estimates will be biased. As a remedy,
robust coefficients should be estimated. Is there any other way to
deal with the problem? Could you explain what you meant in your
answer?

My specific problem is that -xttobit- in contrast to -xtreg- doesn't
have any robust options in Stata. How would you recommend me to reduce
the unwanted effects of heteroskedasticity?

3- You refer to RHS variables in your answer. Do you mean variables of
interest in the set of explanatory variables? With respect to your
suggestion, do you think SYS-GMM will resolve the problems of both
simultaneity and unobserved heterogeneity in my sample?

what are the commands to use first-difference and lagged independent
variables at the same time in Stata, if any?

To be specific, how would compare xtabond, xtabond2 and xtdpdsys with
each others? Which one would you compare? What are the required
conditions to be able to safely use these methods?

4- As to my still remaining question, in a panel data setting, what
pre- and post-tests do you recommend in  general to check for the
underling conditions and assumptions? What can one do to increase the
reliability and validly of the results?

I'm really thankful to your support and will definitely aknowledge
that if my efforts results in any publications. That's the minmum to
compensate your time.....

My kind regards from Holland,
Fardad

On Tue, Oct 7, 2008 at 4:08 PM, Nils Braakmann
<nilsbraakmann@googlemail.com> wrote:
>
> Hello Fardad,
>
> some more comments in addition to Martin's:
>
> > 1) FE, RE, or BE?
> > ***What should I do? What is the valid approach to pursue? How should
> > I justify using RE or BE? Is there any alternative tests or methods
> > that can be used? What specific conditions should I check (and how?)
> > to be sure about using RE for my estimations?
>
> Essentially it depends on whether you believe that you have unobserved
> heterogeneity that is correlated with your variables of interest. If
> that is the case BE and RE will be inconsistent and you should rely on
> the FE estimates (your Hausman tests seem to suggest that). The
> dropped variables are most likely time-constant within firms so there
> is no (within) variance that could be used for estimation. Similarly,
> the insignificance of the remaining variables you refer to is most
> likely caused by too few variation within firms so these effects are
> estimated poorly. There is in fact no simple solution to that. Some
> things that come to my mind: You could either (a) use BE or RE (or
> pooled OLS) (with a lot of control variables to control for as much of
> the unobservables as possible) and acknowledge that your results may
> in fact be caused by unoberserved heterogeneity rather than by your
> variable of interest (and, if possible, include a statement in your
> paper why you do not believe that unobserved heterogeneity is a
> problem in this estimation or provide some explanation on the likely
> direction of the bias) or (b) you could try to find some outside
> instruments that are uncorrelated with the outcome and your unobserved
> heterogeneity but correlated with your variables of interest and apply
> some sort of instrumental variable estimator.
>
>
> > 2) Robust standard errors?
> >
>
> > ***What would you suggest? How would you correct for
> > heteroskedasticity? Is there any other important characteristics that
> > I need to check before I can be sure about the validity and
> > reliability of my results? What pre- or post-tests do you suggest?
>
> Stata now provided clustered standard errors (on the panel id
> variable) when you request the usual robust errors as the latter are
> inconsistent in a panel context (see Stock, James H. und Mark Watson,
> 2008: "Heteroskedasticity-Robust Standard
> Errors for Fixed Effects Panel Data Regression", Econometrica 76(1):
> 155-174). You should use these (for a discussion of standard errors in
> a panel context see e.g. chapter 21.2.3 and the example in chapter
> 21.3.2 in  Cameron, A. Colin and Prvain K. Trivedi, 2005
> "Microeconometrics - Methods and Applications", Cambridge University
> Press).
>
> >3) SYS-GMM method?
> >***How can I successfully implement this method in Stata? Is there any
> >alternatives that you would suggest? In general, how would you correct
> >for simultaneity problem, if you don't have access to good
> >instruments?
>
> System GMM is implemented in -xtabond2- by  David Roodman who has also
> written two(?) "pedagogical" papers on the practical implementation
> (available on the web, don't have the links right now). Stata also has
> several commands: -xtabond- and from 10.0 onwards -xtdpdsys- and
> -xtdpd-. For your general question: In a panel context you might want
> to consider using first differences to get rid of the unobserved
> heterogeneity and then use lags as instruments to get rid of any
> remaining (contemporaneous) correlation between your RHS variables and
> the error. However, this does not solve your problem of too few within
> variation...
>
> Hope this helped.
> Best regards,
> Nils
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