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st: AW: Quantile regression with stata


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: AW: Quantile regression with stata
Date   Fri, 20 Feb 2009 09:20:48 +0100

<> 


Well, http://www.stata-press.com/books/mus.html devote their entire chapter
7 to QR, but postestimation features are not big in there, either. 




HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Tomas M
Gesendet: Freitag, 20. Februar 2009 01:19
An: statalist@hsphsun2.harvard.edu
Betreff: st: Quantile regression with stata

Hello and thank you in advance,

I am using quantile regression to model the 50th percentile for my data.
Unfortunately, the resources are limited on qreg when comparing to the
literature available for traditional regression models.

Questions:

1.  I am mainly focused on the 50th percentile.  But, if I wanted to compare
25th and 75th models (using the sreg with q(0.25 0.50 0.75) option), I am
wondering if it is better to use the same set of predictors for each
percentile, or if I should use a different set of predictors for each
percentile?  I wonder about this since each percentile may have a different
set of significant predictors (for example, age may be significant for the
50th percentile, but not significant for the 25th percentile).  Thus, is it
better to compare models for 25th 50th and 75th percentiles using the best
fitted model with all relevant significant predictors?

2.  My other question pertains to interpretation of coefficients.  When I
run a model with certain predictors, sometimes I get a very small
coefficient (i.e. 5e-15).  How do I interpret this, and what does this mean?
I do notice that this disappears once I collapse the categories for the
predictor.

3. What tools are available to assess goodness of fit for my qreg model?  I
have read through the qreg postestimation commands for stata, and it seems
that linktest, and predict would be my only options (i.e. plots of residuals
versus fitted values are available).  I have also looked through the UCLA
regression with stata web book section on quantile regression, and it also
states that there are limited postestimation commands available.

4.  This final question relates to question 3.  What would be the best
method for variable selection for my final model?  Still would be backwards
elimination?  How would I do this in stata, given the limited availability
of post estimation commmands?  Just start with all variables in my model,
then eliminate ones with p-value greater than 0.05 (or add ones with p-value
less than 0.05 if I were to add stepwise procedures too)?

Any help would be appreciated, as well as links to further references. Thank
you for reading my long post.

Tom

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