I have always felt that many of my colleagues and students use the Huber
robust standard errors without knowing what they actually mean because
that's what everyone in the discipline does. In my intermediate method
course, I try to teach my students that the confidence interval based on
the robust standard error covers the ``wrong'' parameter with the
``correct'' coverage probability, and tell them that maybe they should be
worried first about how wrong your estimator is.

   In any event, I came across an article by David Freedman in the most
recent issue of the American Statistician gives a very nice discussion on
this point, and thought it may be of interest to some people on the
mailing list.

On The So-Called "Huber Sandwich Estimator" and "Robust Standard Errors"
Author: Freedman, David A.1
Source: The American Statistician, Volume 60, Number 4, November 2006, pp.
299-302(4)

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 meaninglessâexcept perhaps as
descriptive statistics.

Best,
Kosuke

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Kosuke Imai               Office: Corwin Hall 041
Assistant Professor       Phone: 609-258-6601
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