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RE: st: RE: Why not always specify robust standard errors?
At 12:26 PM 2/13/2007, Maarten Buis wrote:
If you think your model is correct then it makes no sense to use robust
standard errors. Note that the model assumes no heteroscedasticity in
the population, so the fact that we always find some heteroskedasticity
in our samples is no argument. You could test it of course, but since
we are now in ``purist land'' we would have serious troubles with
performing tests based on the model that was subsequently selected,
since now our conclusions are based on a sequence of tests...
Thanks Maarten. I'm no doubt betraying my statistical ignorance
here, but is that the correct definition of "correct?" i.e. does
"correct" mean no heteroskedasticity? Or is no hetero just a
requirement for OLS to be the optimal method for estimating the
model? It seems to me that a model could be correct in that Y is a
linear function of the Xs and all relevant Xs are included. The
additional requirement of homoskedastic errors is a requirement for
OLS estimates to be BLUE. But, if errors are heteroskedastic, we can
use another method, like WLS. Or, we can content ourselves with
using robust standard errors which do not require that the errors be iid.
In any event, in practice probably every model will be at least a
little mis-specified and/or have error terms that aren't perfectly
iid. So, why not always use robust? One potential problem, I think,
is that robust standard errors tend to be larger. Perhaps
unnecessarily relaxing the iid assumption has similar effects to
including extraneous variables - estimates will remain unbiased but
adding unnecessary junk to the model can cause standard errors to go up.
You know, this is one of the problems with using Stata. I never used
to have these kinds of problems with SPSS, because SPSS doesn't let
you estimate robust standard errors!
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Richard Williams, Notre Dame Dept of Sociology
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