Jian Zhang,
Can you post the output of -hausman- to the list? I think I know what
might be going on, but it's easiest to see by looking at the output.
Cheers,
Mark
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
> Rodrigo A. Alfaro
> Sent: 19 July 2006 04:06
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: RE: a question on testing for random effect
> model against fixed effect model
>
> Is the Hayashi proof valid for unbalanced-panel?
> R
>
> ----- Original Message -----
> From: "Jian Zhang" <jzh@ucdavis.edu>
> To: "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>;
> <statalist@hsphsun2.harvard.edu>; <statalist@hsphsun2.harvard.edu>
> Sent: Monday, July 17, 2006 8:44 PM
> Subject: RE: st: RE: a question on testing for random effect
> model against fixed effect model
>
>
> Thanks, Mark! It seems that the CALCULATED standard hausman
> test statistic is always positive even in FINITE samples
> (i.e., calculated V(b)-v(B) is positive definite) as long as
> one uses same variance estimates (mathematically this is
> proved by Hayashi, 2000, as you
> mentioned:
>
> "This appendix proves that the Avar(q_hat) in (5.2.21) is
> positive definite and the Hausman statistic (5.2.22) is
> guaranteed to be nonnegative in any finite samples."
> (Hayashi, Econometrics (2000), Appendix 5.A, pp. 346-349 and 334-335.)
>
> So by adding option -sigmamore- or -sigmaless-, I did get a
> positive standard hausman test (Chi square).
>
> However, confusing to me is that at the end of the results of
> implementing hausman test in stata there is one line saying
> (V(b)-V(B) is not positive definite) despite that I added option
> -sigmamore- and got a positive Chi square. Any thoughts why
> stata said that? From what I understand, calculated
> V(b)-V(B) should be ALWAYS positive definite as long as one
> uses option
> -sigmamore- or -sigmaless-. The statement made by stata
> results seems to
> contradict the
> mathematical argument made by Hayoshi.
>
> Best regards,
> Jian Zhang
>
> > 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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