# st: Quantile regression with stata

 From Tomas M To Subject st: Quantile regression with stata Date Thu, 19 Feb 2009 16:18:52 -0800

```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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