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RE: RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weak instruments)


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   RE: RE : Heteroskedasticity and fixed effects (was: st: RE: Re: Weak instruments)
Date   Thu, 17 Jul 2008 16:58:19 +0100 (BST)

I was responding to the statement that "In practice, it just makes more
sense to always use robust standard errors." I would guess that your
category 3 is a relatively rare category and would certainly not
warrant such a general advise. Basically, my point is a variation on
the advise that whenever one thinks one has found heteroskedasticity
one should first take a good long look at whether the model is
correctly specified, instead of directly jumping at wls, robust, or
other such methods.

-- Maarten

--- Gaulé Patrick <patrick.gaule@epfl.ch> wrote:
> > > In both cases where is the harm in using robust standard errors
> > > and  what's the point to test for heteroskedasticity?

--- Maarten buis 
> > The harm comes from making people feel more secure about 
> > their results than they should be. The point made by Freedman 
> > is that it is not going to do them any good, but only the 
> > name -robust- suggest that they are somehow protected against 
> > all kinds of evils.

-- "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk> wrote:
> You don't mean this literally, right?  For example, if you think a
> linear model is reasonable and you want to use OLS, but you don't
> want to rely on more assumptions than you really need, then using OLS
> + heteroskedastic-robust standard errors (instead of OLS + classical
> SEs) can't hurt and - if heteroskedasticity is actually present -
> could help.  This counts as "doing them some good", I think.
> 
> Or to repeat Patrick's points 1 and 2, and to make explicit the
> implicit point 3:
> 
> 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
> 
> 3) If the model is mostly correct, but the assumption of
> homoskedasticity is implausible, undesirable, or unsupported, then
> robustifying helps.



-----------------------------------------
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/
-----------------------------------------


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