Thanks Mark, now things are much more clear.
Quoting "Schaffer, Mark E" <[email protected]>:
> Alvaro,
>
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]]
> > Sent: 07 August 2007 18:18
> > To: [email protected]
> > Subject: st: Suest and Sureg
> >
> > Dear Stata list,
> >
> > maybe this a very basic question. I`m trying to estimate a
> > model using SUR, this technique is new for me. My problem is
> > that command sureg maybe is not taking into account posible
> > heteroskedasticity. Then I have tried suest after regress
> > (someone told me that with this command is possible to run a
> > sur under heterosk on each equation). I notice that the
> > standard errors have change and are very similar to my
> > independent equations regression once adjusted using robust
> option.
> > However, i dont know if this command is really doing a sur
> > regression allowing for correlations among unobservables. I
> > have read some applications that say that is the "correct"
> > way to run that kind of regression (sur model corrected by
> > white). However in other applications i have read that this
> > command is not properly a sur regression. A previous post I
> > have read the following
> >
> > "Just to add a bit to Maarten's suggestion: -suest- will let
> > you combine two or more "seemingly unrelated" equations so
> > that you can test cross-equation restrictions and the like.
> > But it won't do "seemingly-unrelated estimation" a la Zellner
> > and -sureg-, i.e., you won't get the efficiency gains
> > possible from estimating the equations as a system. The
> > coefficients reported by -suest- are just the original ones"
> >
> > So, my doubt now is bigger. I only want to obtain the correct
> > variance covariance matrix in order to test corss equation
> > hypothesis under to kind of models. The first one uses the
> > same covariates for all the equations and the second one
> > different covariates. Both are OLS-type.
>
> The comment above was by me.
>
> The way to understand what is going on is to think in terms of
> efficiency vs. robustness. You get efficiency by modelling the
> heteroskedasticity, cross-equation correlations, etc. correctly, and
> incorporating these into GLS-type estimates of your coefficients.
> You
> get robustness by using a covariance estimator that is robust to
> heteroskedasticity etc.
>
> -sureg- does traditional SUR. This is GLS-type estimation that
> takes
> account of cross-equation correlations to get more efficiency.
> Since
> the cross-equation correlations are modelled, you can test cross-eqn
> restrictions and the like. But -sureg- assumes homoskedasticity, and
> if
> the errors are heteroskedastic, then the SEs reported by -sureg- will
> be
> wrong.
>
> -suest- applies an Eicker-Huber-White-sandwich covariance estimator
> to a
> set of equations estimated by, in your case, OLS. You don't get the
> efficiency that you would get if you modelled the cross-eqn
> correlations
> (like SUR), or for that matter, the efficiency that you would get if
> you
> modelled the heteroskedasticity and did GLS. But your SEs will be
> valid
> whatever the cross-equation correlations or heteroskedasticity that
> you
> face.
>
> Maybe you want to combine these, or perhaps do SUR with with
> modelled
> heteroskedasticity. I suppose this is possible, but not with the
> canned
> estimators available in official Stata. You would have to program
> them
> yourself or find someone else that has already programmed them.
>
> Your options in brief: if you are worried about heteroskedasticity,
> then
> -suest- is your only choice; if you aren't worried about
> heteroskedasticity, then both -suest- and -sureg- generate valid
> SEs,
> but -sureg- is more efficient.
>
> Cheers,
> Mark
>
> > hope someone can answear me more and if you need more
> > information I can explain the details of my model.
> >
> > Thanks a lot
> >
> > Alvaro
> >
> >
> >
> >
> >
> >
> >
> >
> > *
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> > * 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.ats.ucla.edu/stat/stata/
>
*
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