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


From   Nick Cox <njcoxstata@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 16:41:10 +0000

We can converge by holding fast to the idea that regression is about
modelling the conditional mean. If you have a inappropriate model for
the conditional mean, wondering how well you can fit it is not a very
interesting or useful question.

Nick

On Mon, Dec 17, 2012 at 4:37 PM, JVerkuilen (Gmail)
<jvverkuilen@gmail.com> wrote:
> 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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