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st: RE: a question on testing for random effect model against fixed effect model


From   "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: a question on testing for random effect model against fixed effect model
Date   Sun, 16 Jul 2006 10:27:14 +0100

Jian,

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu 
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Jian Zhang
> Sent: 16 July 2006 08:36
> To: statalist@hsphsun2.harvard.edu
> 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.)

Sorry to come in late on this, but I have three suggestions relating to
your original question.

First, in a standard (i.e., non-robust) Hausman test, you can guarantee
a positive test statistic by using the -sigmamore- or -sigmaless-
options; the former is more traditional.  Second, including the constant
isn't traditional in a fixed vs. random effects hausman test.  Third, if
you want to do a heteroskedastic- or cluster-robust version of the test,
you can use the artificial regression version of the test described in
Wooldridge's 2002 book (and I believe discussed in Statalist last year
by Vince Wiggins, if I'm not mistaken) and use robust or cluster-robust
standard errors in the artificial regression.  The artificial regression
version will also guarantee a positive test statistic (of course!).

Cheers,
Mark

Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3296
email: m.e.schaffer@hw.ac.uk
web: http://www.sml.hw.ac.uk/ecomes


> 
> 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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