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st: RE: xtreg vs. xtgls vs. xtpcse vs. xthtaylor

From   "Rodrigo Alfaro A." <>
To   <>
Subject   st: RE: xtreg vs. xtgls vs. xtpcse vs. xthtaylor
Date   Wed, 5 Nov 2008 10:14:01 -0300



(a) -xtreg, robust- changes the standard errors without changing the point estimate. This is a sort of White standard error correction. Here you believe that your results are right but the inference could be misleading.
(b) -xtgls- and -xtpcse- are some kind of GLS estimator. However, the assumptions behing each one of them are different. As a rule of thumb -xtgls- provides smaller std errors than -xtpcse- then later is prefered if you are willing to use GLS. I am not a fan of GLS estimators. In those you must impose some structure to the model that could be complete wrong. That structure affects point estimates!

(c) -xthtaylor- is an IV estimator with random effect. It is a special case of -xtreg, re- when you have some endogenous variables which could be time invariant. 

I suggest to have in hand Longitudinal Panel Data manual (StataCorp) and Econometric Analysis of Cross Section and Panel Data (Wooldrige, 2002). Try to get a sense of what you need for your problem and repost your question giving some brief reference of your empirical application.

HTH, Rodrigo.


-----Mensaje original-----
De: [] En nombre de Fardad Zand
Enviado el: Miércoles, 05 de Noviembre de 2008 09:48 a.m.
Asunto: st: xtreg vs. xtgls vs. xtpcse vs. xthtaylor

Dear listers,

I couldn't figure out exactly what the difference is between

(a) xtreg, vce(robust) cluster(id)
(b) xtgls
(c) xtpcse

To my knowledge they all try to correct some form of heteroskedasticity or panel specific correlations. But it is not clear to me where and when each option is best to use.

Moreover, after you run a model on panel data, how can you check:

(1) heteroskedasticity
(2) auto-correlation
(3) endogeneity
(4) multicollinearity

One probably need the above information to decide what model/estimator to use.

I really appreciate your time and supporting me in my research.

Good luck to all of you,

ir. Fardad Zand
Researcher & PhD Candidate
MSc Management of Technology

Department of Economics & Management of Innovation Faculty of Technology, Policy & Management Delft University of Technology

Kruisstraat 30
2611 MJ Delft
the Netherlands

Office: +31 15 278 42 73 (room C0.060)
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