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From |
Alvaro Monge Zegarra <A.G.Monge-Zegarra@sussex.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Suest and Sureg |

Date |
Wed, 08 Aug 2007 02:30:15 +0100 |

Thanks Mark, now things are much more clear. Quoting "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>: > Alvaro, > > > -----Original Message----- > > From: owner-statalist@hsphsun2.harvard.edu > > [mailto:owner-statalist@hsphsun2.harvard.edu] > > Sent: 07 August 2007 18:18 > > To: statalist@hsphsun2.harvard.edu > > 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 > > > > > > > > > > > > > > > > > > * > > * 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: > * 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: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: RE: Suest and Sureg***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

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