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From |
Gaulé Patrick <patrick.gaule@epfl.ch> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weakinstruments) |

Date |
Thu, 17 Jul 2008 16:34:45 +0200 |

David Freedman's points are certainly interesting but I don't see how they contradict the suggestion that in practice it makes more sense to use robust standard errors rather than homoscedastic errors. If I get his and your points correctly: 1) If the model is seriously in error, robustifiying will not help getting better estimates of the coefficients. Getting standard errors right is irrelevant. 2) If the model is nearly correct, robustifying makes virtually no difference In both cases where is the harm in using robust standard errors and what's the point to test for heteroskedasticity? Best regards, Patrick Gaulé ________________________________________ De : owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] de la part de Maarten buis [maartenbuis@yahoo.co.uk] Date d'envoi : jeudi 17 juillet 2008 14:22 À : statalist@hsphsun2.harvard.edu Objet : Re: RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weak instruments) --- Gaulé Patrick <patrick.gaule@epfl.ch> wrote: > I would not worry about testing for heteroskedasticity. In practice, > it just makes more sense to always use robust standard errors. David Freedman (2006) has exactly the opposite view. He basically distinguishes two scenarios: 1) the model is very wrong in which case robustifying the standard errors makes a difference, but it also means that all the coefficients are also wrong. So in this case you are correctly testing meaningless hypotheses. 2) The model is almost right, in which case robustifying makes virtually no difference. In other words -robust- either makes no difference or when -robust- does make a difference, the model is so much beyond repair that you'll do the wrong thing anyhow. So, all -robust- does is add a false sense of security. A final note, though you will probably already know it as it is (or should be) in any intro stats book: Do not test for heteroskedasticity before you have looked at the functional form of the effects of your predictors. -- Maarten David A. Freedman (2006) "On the So-Called `Huber-Sandwich Estimator' and `Robust Standard Errors'". The American Statistician 60(4):299--302. ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weak instruments)***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

**Re: RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weak instruments)***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weakinstruments)***From:*Gaulé Patrick <patrick.gaule@epfl.ch>

**Re: RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weak instruments)***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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