--- Richard Williams wrote:
> So, I think his cautions and concerns are pretty valid; robust
> standard errors are not a panacea for a model that is "seriously in
> error" . But, it still seems to leave open the question of whether
> always using robust would be a good idea if your model is "nearly
> correct" or hetero is an issue.
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...
So it depends on how much faith you have in your model. On the other
hand I think it is good that Stata forces us to make a conscious
decision to declare that there is something wrong with the error but
that we don't care about it. Since trying to understand why there is
something wrong with the errors improves our understanding of the data
even if we finally choose to ignore it by specifying the robust option.
-----------------------------------------
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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