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Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?


From   "JVerkuilen (Gmail)" <jvverkuilen@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Mon, 17 Dec 2012 11:37:29 -0500

On Mon, Dec 17, 2012 at 10:27 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:

The whole point of robust standard errors is not that it
> "solves" in some way for heteroskedasticity, it just makes that
> "assumption" irrelevant. For more, see section 20.20 of the User's
> Guide.

I think I'm vehemently agreeing with you Maarten, but "sort of
irrelevant" is probably a better characterization. Heteroscedasticity
is often the sign of a very bad model. There's a temptation to "white
wash" a bad model by using remedial measures such as one of the
various heteroscedasticity-consistent VCE estimates, bootstrap,
jackknife, etc., but if the model is not appropriate all the remedial
measures in the world won't truly fix it up.
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