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st: 2SLS,fe & two-way error component_hetero&autoco?


From   "Svetlana Mira " <SvetlanaM@Cardiff.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   st: 2SLS,fe & two-way error component_hetero&autoco?
Date   Wed, 26 May 2004 19:13:59 GMT0BST

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. 

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: clive.nicholas@ncl.ac.uk
> Newcastle University  |http://www.ncl.ac.uk/geps
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