Steve,
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Steven Archambault
> Sent: 27 June 2009 00:26
> To: [email protected]; [email protected];
> [email protected]
> 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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