Re: st: QREG Question

["Santos Silva, J.M.C." <jmcss@essex.ac.uk>]

Dear Patrick,

I agree with most of what has been said but I have some additional remarks/clarifications.

- qreg is not suitable for your data because standard quantile regression is valid only for 
continuous data, which obviously is not your case.  In case your data are counts, you can 
use the package qcount by Alfonso Miranda which implements a quantile regression 
estimator for count data (type: ssc install qcount).

- I fully agree that Poisson regression is a good starting point for the data you have. Nick 
directed you to the post by Bill Gould on his blog but I think the essential reference on this 
topic is this:  http://privatewww.essex.ac.uk/~jmcss/LGW.html

- A two-part model, as Nick suggested, may be an interesting alternative to Poisson if you 
believe that  zeros and the positive observations are generated by two different processes. 
However, if you are not working with count data avoid using negative binomial or zero-inflated 
models because their results are not invariant to the scale of the data.

Hope this helps,

Joao

> 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 dependent into quantles?
> 
> Cheers,
> 
> Patrick Behrer
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