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Re: st: RE: Why not always specify robust standard errors?


From   Richard Williams <Richard.A.Williams.5@ND.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Why not always specify robust standard errors?
Date   Tue, 13 Feb 2007 12:03:20 -0500

At 11:42 AM 2/13/2007, German Rodriguez wrote:
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.
Thanks German. I've read the paper (it is only 4 pages long but to me reads more like it is 40!). If I understand it correctly, he says people who use robust standard errors make this big deal about getting the standard errors right, but they pay little attention to the fact that their parameter estimates are biased, sometimes seriously so.

But, as you note, he also says "If the model is nearly correct, so are the usual standard errors, and robustification is unlikely to help much." And, he also seems to make a distinction between using robust standard errors when your model is mis-specified, and using them when the model is correctly specified but heteroskedasticity is present (at least that is how I interpret part 6 of his paper.)

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.


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Richard Williams, Notre Dame Dept of Sociology
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