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.