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RE: st: RE: Hausman taylor


From   "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Hausman taylor
Date   Sat, 29 Apr 2006 07:39:07 +0100

Julia,

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu 
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Julia Spies
> Sent: 28 April 2006 23:51
> To: statalist@hsphsun2.harvard.edu
> 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
> 
> 
> > --- Ursprüngliche Nachricht ---
> > Von: "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>
> > An: <statalist@hsphsun2.harvard.edu>
> > Betreff: st: RE: Hausman taylor
> > Datum: Fri, 28 Apr 2006 22:51:29 +0100
> > 
> > Julia,
> > 
> > > -----Original Message-----
> > > From: owner-statalist@hsphsun2.harvard.edu
> > > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Julia 
> > > Spies
> > > Sent: 28 April 2006 12:48
> > > To: statalist@hsphsun2.harvard.edu
> > > 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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