Sorry, what I meant was the the overid test stats is not significant and
running a hausman test to compare HT with GLS is significant. I just mixed
it up. Apologies!
Julia
> --- Urspr�ngliche Nachricht ---
> Von: "Schaffer, Mark E" <[email protected]>
> An: <[email protected]>
> Betreff: RE: st: RE: Hausman taylor
> Datum: Sat, 29 Apr 2006 07:39:07 +0100
>
> Julia,
>
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of Julia Spies
> > Sent: 28 April 2006 23:51
> > To: [email protected]
> > Subject: Re: st: RE: Hausman taylor
> >
> > 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.
>
> Apologies if I am misunderstanding what you have reported, but it's the
> other way around. A large and significant overid stat is evidence AGAINST
> your HT estimate. As usual with IV estimation, under the null that the
> orthogonality conditions are statisfied (the instruments are "valid"), the
> overid stat is distributed as chi-sq. A big stat and rejection of the
null
> suggests that your orthogonality conditions are not satisfied, i.e., the
> instruments are not valid, i.e., your HT estimation is misspecified.
>
> --Mark
>
> > 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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