Jian,
The battle here -between Fixed (FE) and Random Effects (RE)- has
some shortcuts that I think are interesting. (1) RE fails completly when
some of the regressor is correlated with the unobservable, (2) RE has
a more complex structure for unbalanced panel in compare with FE
that the correction is pretty standard, (3) RE assumes some specific
structure of the error term, this can be improved by a second-step
of robust matrix for standard errors (Mark Schaffer pointed me this
in a discussion about Hausman-Taylor estimator which is RE) but
this way carries out a lot of noise in compare with a simple std-error
correction in the case of FE. (3) FE is not efficient, but can be improved
by a standard error correction, such as Newey-West type. You can
estimate the FE with std-error correction using -xtivreg2- for that type
-findit xtivreg2- and follow the instructions.
Conclusion, it seems to me that FE is "more" robust than RE for
several situations. Indeed, some recent working papers show
preference for FE instead of RE in nonlinear models, such as logit,
probit or tobit.
I hope this helps you,
Rodrigo.
----- Original Message -----
From: "Jian Zhang" <jzh@ucdavis.edu>
To: <statalist@hsphsun2.harvard.edu>
Sent: Sunday, July 16, 2006 3:35 AM
Subject: st: a question on testing for random effect model against fixed
effect model
Thanks, Clive and Rodrigo!
I wonder if there is an alternative test for random effect against fixed
effect or a robust form of hausman test if the assumptions made for
Hausman test do not hold (one of the assumptions for hausman test
is the homoskedasticity and uncorrelation of the idiosyncratic errors.
But this is often invalid.)
Jian
On Sat, 15 Jul 2006, Rodrigo A. Alfaro wrote:
> Jian,
>
> Try -xtreg, re sa- instead of -xtreg, re- the additional option takes
> care "more carefully" the unbalanced issue using Swamy-Arora method.
>
> Read Method and Formulas in the manual, for version 8:
> http://www.stata-press.com/manuals/stata8/xtreg.pdf and version 9:
> http://www.stata.com/bookstore/pdf/xtreg.pdf
>
> Rodrigo.
>
>
> ----- Original Message -----
> From: "Clive Nicholas" <Clive.Nicholas@newcastle.ac.uk>
> To: <statalist@hsphsun2.harvard.edu>
> Sent: Saturday, July 15, 2006 4:19 AM
> Subject: Re: st: a question on testing for random effect model against
> fixed
> effect model
>
>
> Jian Zhang wrote:
>
> > I have a question on testing random effect model against fixed effect
> > model. Hope that you can help me out. Here is the question;
> >
> > I am applying random effect model and fixed effect model to an
> > unbanlanced panel data (use xtreg, re and xtreg, fe). To test which
> > model is more appropriate, I run a hausman test. However, the test
> > statistics (the chi square) is negative. This makes hausman testing
> > impossible, since chi square cann't be negative. The reason that
> > hausman
> > test doesn't work is that the model's error structure does not meet the
> > assumptions made for the hausman test.
>
> [...]
>
> Did you run the following:
>
> xtreg ..., fe
>
> est store fixed
>
> xtreg ..., re
>
> hausman fixed ., alleqs constant
>
> If not, see if that works. Works for me every time I have to use it.
>
> CLIVE NICHOLAS |t: 0(044)7903 397793
> Politics |e: clive.nicholas@ncl.ac.uk
> Newcastle University |http://www.ncl.ac.uk/geps
>
> Whereever you go and whatever you do, just remember this. No matter how
> many like you, admire you, love you or adore you, the number of people
> turning up to your funeral will be largely determined by local weather
> conditions.
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