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Re: st: QREG Question


From   Nick Cox <njcoxstata@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: QREG Question
Date   Sat, 28 Jul 2012 13:55:58 +0100

There's some confusion here between what to do when a response variable has a highly skewed distribution and what functional form is appropriate to express the relationship between that response and a bundle of predictors.

By implication Arnold has a response that is expected to have positive mean but is often zero in practice. I'd expect, with no further information, that -qreg- might perform a bit better than -regress- in this situation, but I'd also expect that -poisson- would do better than either. See also -glm, link(log)-. Seek out Bill Gould's blog posting on Poisson at blog.stata.com

However, if some or all of the zeros come from a distinct group you may need a two-part model. For example, zero hours spent watching the Olympics on TV may reflect zero interest and/or zero access to a TV.

Either way no linear combination Xb has "correcting for skewness" as its direct purpose.

What -qreg- does is well described in the documentation but by default it models the conditional median.

Nick

On 27 Jul 2012, at 20:33, Arnold Behrer <behrer@post.harvard.edu> wrote:

Hello,

I have two questions, one related to stats generally and the other to
Stata specifically.

The first is about how useful quantile regression is in correcting for
skewed data.  I have a data set heavily skewed to the right.  I have
considered log transforming it but I would rather not because the
skewness is due in large part to zero values that are meaningful in
the context of the regression.  I thought that perhaps quantile
regression might be a way of correcting for this skew without dropping
the zero values.  Is that true?

The second question is simply whether the Qreg command in Stata runs a
normal quantile regression - splitting the
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