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From |
"JVerkuilen (Gmail)" <jvverkuilen@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Quantile Regression |

Date |
Tue, 2 Oct 2012 22:12:59 -0400 |

On Tue, Oct 2, 2012 at 7:31 PM, Steve Samuels <sjsamuels@gmail.com> wrote: > > Without details (see FAQ 3.3 first sentence), we can only guess. This > could happen if 1) you did not set the same random seed before each > -sqreg- and -bsqreg- command; 2) the number of bootstrap replicates > differed between -sqreg- and -bsqreg- runs; or 3) -sqreg- does not > rejects replicates in which convergence failed for any quantile. If the standard errors are different it's no great surprise if you're running bootstrap. All the stuff said makes sense. Check on a known dataset (such as auto) and fix the seed. > By the way, the manual states that -sqreg- is faster than -bsqreg-. I believe that computationally there are some speedups due to the fact that the linear program can be solved for one and simply updated to get the rest of the quantiles, but I could be mistaken. Roger Koenker's book (Quantile Regression, Oxford University Press, 2006) discusses computation in detail. Also there are analytic options to bootstrapping that might be much faster. -qreg- generates standard errors analytically using a weighting matrix and density estimator of the residuals. . sysuse auto . qreg price mpg Median regression Number of obs = 74 Raw sum of deviations 142205 (about 4934) Min sum of deviations 129521.7 Pseudo R2 = 0.0892 ------------------------------------------------------------------------------ price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mpg | -135.6667 67.26576 -2.02 0.047 -269.7585 -1.574816 _cons | 8088.667 1483.808 5.45 0.000 5130.749 11046.58 ------------------------------------------------------------------------------ . bsqreg price mpg, reps(999) *note that bsqreg defaults to 20!?!?!?! Median regression, bootstrap(999) SEs Number of obs = 74 Raw sum of deviations 142205 (about 4934) Min sum of deviations 129521.7 Pseudo R2 = 0.0892 ------------------------------------------------------------------------------ price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mpg | -135.6667 35.63527 -3.81 0.000 -206.7043 -64.62906 _cons | 8088.667 889.0486 9.10 0.000 6316.381 9860.953 ------------------------------------------------------------------------------ In this case the standard errors are markedly different and playing with the different methods in -qreg- gives quite different values, but I don't really know enough to be able to comment on why. I am inclined to trust the bootstrapped ones because this problem has a rather small N. I suspect that it is very slow on a huge problem though, given that it needs to sort the residuals. Koenker did a good deal of work on alternatives such as inverting a test of some sort; I think the R implementation of quantile regression has this. Again see his book. > I've never had the luxury of having so many observations to analyze. I > imagine that almost every simple model can be rejected, so that model > building and validation are real challenges. Randomly subsample and do a real cross validation? Jay -- JVVerkuilen, PhD jvverkuilen@gmail.com "Out beyond ideas of wrong-doing and right-doing there is a field. I'll meet you there. When the soul lies down in that grass the world is too full to talk about." ---Rumi * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Quantile Regression***From:*Robert Davidson <rhd773@gmail.com>

**References**:**st: Quantile Regression***From:*Robert Davidson <rhd773@gmail.com>

**Re: st: Quantile Regression***From:*Steve Samuels <sjsamuels@gmail.com>

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