David Freedman has a provocative answer in "On the so-called 'Huber sandwich
estimator' and 'robust standard errors' in the American Statistican, Vol 60
(4) 299-302, November 2006. Here is the abstract:
The "Huber Sandwich Estimator" can be used to estimate the variance of the
MLE when the underlying model is incorrect. If the model is nearly correct,
so are the usual standard errors, and robustification is unlikely to help
much. On the other hand, if the model is seriously in error, the sandwich
may help on the variance side, but the parameters being estimated by the MLE
are likely to be meaningless - except perhaps as descriptive statistics.
I think he has a valid point asking why the fuzz about standard errors when
the estimates may be wrong.
-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Richard Williams
Sent: Tuesday, February 13, 2007 11:23 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: Why not always specify robust standard errors?
A student asked me a question the other day that I couldn't think of
a definitive answer for: Why not always specify -robust- when using
OLS regression? My initial reaction is to say that you shouldn't
relax restrictions unnecessarily; and there are various
post-estimation commands where Stata will at least whine at you if
you've used robust standard errors (e.g. -lrtest-). But in practice,
your model is probably at least a little mis-specified and/or there
may be some degree of heteroskedasticity, so maybe robust is a good
idea. Any thoughts on the matter?
Incidentally, my own experience is that robust standard errors
usually aren't all that different from non-robust standard errors. Is
that what other people have found as well, or is just me?
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Richard Williams, Notre Dame Dept of Sociology
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