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Re: st: Modeling % data

From   Austin Nichols <>
Subject   Re: st: Modeling % data
Date   Thu, 23 Sep 2010 14:12:53 -0400

-qreg- requires no "fix" using higher powers of X in the general case.

I referred the original poster to -glm- as well, in
but your objection to -qreg- is unfounded in the case I outlined--if X
is a continuous variable (did you mean unbounded, maybe?) there is no
reason it cannot have a linear effect on the conditional median of y,
even if y is bounded between 0 and 1, and even if there is a nonzero
fraction at the boundaries.  Of course, if a significant fraction of
the data piles up at the boundary, neither -qreg- nor -glm- will be a
particularly good model, and the typical researcher may prefer a MLE
that has a two-part flavor to it (requiring some strong assumptions
about the distribution of errors).

On Wed, Sep 22, 2010 at 12:19 PM, Maarten buis <> wrote:
> --- Austin Nichols wrote:
>> I don't see how data approaching the boundaries is a problem in
>> -qreg-, as long as the fraction at the boundary itself is not too
>> large (though that in itself is more an indictment of the outcome
>> measure than a necessary problem for quantile regression).  If 10% of
>> the outcomes are at the lower boundary (zero) for low X and 10% of the
>> outcomes are at the upper boundary (100) for high X, how is that a
>> problem for estimating how the conditional median changes with X?
> The problem would be that in those cases is that if X is an continuous
> variable it is probably not going to have a linear effect. That is
> what the boundary does. If you are approaching one boundary, than you
> might get away with adding squares, but if you are approaching both
> boundaries, like in the case of the original question, things would
> get much harder (though not impossible). However, in those case I
> would just go for models in Stata that were written for this type of
> data like the ones I refered to earlier, rather than try to "fix"
> -qreg-.

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