Dear Mark,
with "improving the model" I mean that the over-identification test
statistic comparing the FE model (I use areg with the cluster() option,
since i identified autocorr. and heteroskedasticity) with the HT estimation
is significant, which means - if I understand it correctly - that the
correlation between the explanatory variables and the individual effects has
been removed by the instrumentation. Of course, since I have the odd
parameter estimates in the instrumented time-invariant variables (which
cannot be estimated in the FE model), they don't enter the
over-identification test.
My question therefore was whether autocorr. and heteroskedasticity could
produce these very high estimates or whether someone could think of any
other source for the problem, and how I can correct for it in the HT
estimation. Sorry for not making my point clear in the first e-mail. I will
definitely try out Rodrigo's suggestions. Thank you very much for the
advice!
Best regards,
Julia
> --- Urspr�ngliche Nachricht ---
> Von: "Schaffer, Mark E" <[email protected]>
> An: <[email protected]>
> Betreff: st: RE: Hausman taylor
> Datum: Fri, 28 Apr 2006 22:51:29 +0100
>
> Julia,
>
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of Julia Spies
> > Sent: 28 April 2006 12:48
> > To: [email protected]
> > Subject: st: Hausman taylor
> >
> > Dear all,
> >
> > I'm quite a beginner with Stata and i'm trying to run a
> > Hausman taylor regression. However, taking some (plausible)
> > time-invariant variables as endogeneous results in outrageous
> > parameter estimates for these variables.
> > Nevertheless, the over-identification test suggests that
> > instrumenting these variables has improved the model.
>
> This sounds odd ... what do you mean by "improving the model"?
>
> --Mark
>
> > Does
> > anyone have an idea what the problem could be? I understand
> > there is no option to correct for heteroskedasticity and
> > autocorrelation. Does anyone know how to do it manually?
> >
> > Cheers,
> > Julia
> >
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> >
> >
>
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