Svetlana,
Quoting Svetlana Mira <[email protected]>:
> Dear Stata-Users,
>
> Has any one tested for autocorrelation and heteroskedasticity in a
> 2SLS, fe and two way error component
> model? I am working with a three dimensional (firm, analyst and
> year), large (44th obs) and unbalanced
> panel data. The model I am running suffers from endogeneity as
> well. Therefore, I intend to evaluate
> and compare my model base on fixed/random effect model, 2SLS, fe and
> two way error component
> model. I have all the reasons to suspect for autocorrelation and
> heteroskedasticity in the 2SLS, fe and
> two-way error component model. Based on xttest3 and pantest2 tests
> I have determined that my fixed
> effect model suffers from both heteroskedasticity and
> autocorrelation. But how would I test for these
> problems in the 2SLS, fe and two-way error component model? I have
> tried ivhettest, but it does not
> work with the fe.
David Roodman's -abar- might be close to what you want. It will test for
AR(1) or AR(2) following estimation using regress, ivreg or ivreg2, or
newey or newey2. The test is robust to the presence of
heteroskedasticity. I think you should be able to transform your data so
that everything is in mean deviation form, estimate using ivreg or ivreg2,
and then use -abar-. For that matter, you could use -ivhettest- after the
same estimation (but the results won't be robust to the presence of
autocorrelation).
Hope this helps.
--Mark
>
> Any suggestions would be very welcomed.
> Thanks,
> Svetlana
>
> On 26 May 2004 at 16:36, Clive Nicholas wrote:
>
> > Kit,
> >
> > Thanks very much for replying. Just a few notes:
> >
> > > If region is a categorical variable, and these are xt data, then
> there are
> > > two possibilities: region modifies the constant term (in which
> some sort
> > > of fe or re model should be used) or region modifies the
> entire
> > > relationship (including the coeff on midch). In the latter case
> a set of
> > > interacted dummies would be used in a fe context, or one could
> use some
> > > sort of random-coefficients model (Hildreth-Houck).
> >
> > Of course, I used REGION as an example. In terms of continuous
> 'third'
> > covariates, does the method change? I've been using OLS (when
> the
> > Gauss-Markov assumptions have been satisfied) or FGLS up until
> now. Most
> > of the explantory variables in my models (i.e., net turnout rates
> and
> > party competition) are continuous.
> >
> > > I did not respond to the original enquiry since the answer
> seemed obvious:
> > > if there is a third variable that (one suspects) should be in
> the
> > > relationship, and it is measurable, the correct methodology is
> to include
> > > it. After having done so, one may test for its relevance.
> Techniques such
> > > as dealing with proxy issues would only arise if the variable in
> question
> > > is not quantifiable.
> >
> > I want to shriek my reply to this, but I'll simply say "I agree
> with all
> > of the above!" That's what I've been doing all along. It was a
> critical
> > query of part of my work that that brought on doubts that I was
> modelling
> > my variables of interest in the correct way.
> >
> > CLIVE NICHOLAS |t: 0(044)191 222 5969
> > Politics |e: [email protected]
> > Newcastle University |http://www.ncl.ac.uk/geps
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
>
>
> *
> * For searches and help try:
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> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3008
email: [email protected]
web: http://www.sml.hw.ac.uk/ecomes
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