Another one that didn't go through the first time. Again, I hope it isn't too late to be useful to Richard or somebody out there.
> -----Original Message-----
> From: Schaffer, Mark E
> Sent: 14 February 2007 08:45
> To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
> Subject: AW: Why not always specify robust standard errors?
>
> Richard,
>
> To answer your second question, White's general test for
> heteroskedasticity - based on the levels, squares and
> crossproducts of the indep vars etc. - is, intuitively, a
> comparison of the elements of the standard, non-robust VCV,
> and the robust VCV. The squares correspond to the diagonals
> of the two VCVs, etc. If you look at just the squares, you
> are looking for a particular kind of heteroskedasticity - the
> kind that affects the SEs. So your informal observation is a
> kind of test for heteroskedasticity, and your observation is
> that you don't often find heteroskedasticity that is so
> severe that you can see it in an informal comparison of the
> two kinds of SEs.
>
> I *think* this is right - I am far away from my books at the
> moment....
>
> Cheers,
> Mark
>
>
> -----Ursprüngliche Nachricht-----
> Von: owner-statalist@hsphsun2.harvard.edu im Auftrag von
> Richard Williams
> Gesendet: Di 2/13/2007 4:22
> An: statalist@hsphsun2.harvard.edu
> Betreff: 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?
>
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
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>
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