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Re: st: RE: Hausman test for clustered random vs. fixed effects (again)


From   Steven Archambault <archstevej@gmail.com>
To   statalist@hsphsun2.harvard.edu, M.E.Schaffer@hw.ac.uk
Subject   Re: st: RE: Hausman test for clustered random vs. fixed effects (again)
Date   Sat, 27 Jun 2009 16:44:14 -0600

Thanks Mark,

I am planning to use it, citing your references. My primary
reservation is that very few applied papers analyzing unbalanced panel
data seem to use this approach. I believe though, that it is a solid
approach.

-Steve

On Sat, Jun 27, 2009 at 11:31 AM, Schaffer, Mark E<M.E.Schaffer@hw.ac.uk> wrote:
> Steve,
>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
>> Steven Archambault
>> Sent: 27 June 2009 00:26
>> To: statalist@hsphsun2.harvard.edu; austinnichols@gmail.com;
>> Alfred.Stiglbauer@oenb.at
>> Subject: st: Hausman test for clustered random vs. fixed
>> effects (again)
>>
>> Hi all,
>>
>> I know this has been discussed before, but in STATA 10 (and
>> versions before 9 I understand) the canned procedure for
>> Hausman test when comparing FE and RE models cannot be run
>> when the data analysis uses clustering (and by default
>> corrects for robust errors in STATA 10).
>> This is the error received
>>
>> "hausman cannot be used with vce(robust), vce(cluster cvar),
>> or p-weighted data"
>>
>> My question is whether or not the approach of using xtoverid
>> to compare FE and RE models (analyzed using the clustered and
>> by default robust approach in STATA 10) is accepted in the
>> literature. This approach produces the Sargan-Hansen stat,
>> which is typically used with analyses that have
>> instrumentalized variables and need an overidentification
>> test. For the sake of publishing I am wondering if it is
>> better just not to worry about heteroskedaticity, and avoid
>> clustering in the first place (even though heteroskedaticity
>> likely exists)? Or, alternatively one could just calculate
>> the Hausman test by hand following the clustered analyses.
>>
>> Thanks for your insight.
>
> It's very much accepted in the literature.  In the -xtoverid- help file,
> see especially the paper by Arellano and the book by Hayashi.
>
> If you suspect heteroskedasticity or clustered errors, there really is
> no good reason to go with a test (classic Hausman) that is invalid in
> the presence of these problems.  The GMM -xtoverid- approach is a
> generalization of the Hausman test, in the following sense:
>
> - The Hausman and GMM tests of fixed vs. random effects have the same
> degrees of freedom.  This means the result cited by Hayashi (and due to
> Newey, if I recall) kicks in, namely...
>
> - Under the assumption of homoskedasticity and independent errors, the
> Hausman and GMM test statistics are numerically identical.  Same test.
>
> - When you loosen the iid assumption and allow heteroskedasticity or
> dependent data, the robust GMM test is the natural generalization.
>
> Hope this helps.
>
> Cheers,
> Mark (author of -xtoverid-)
>
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>
>
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