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From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Quantile vs Quartile regression |

Date |
Tue, 28 May 2013 23:38:18 -0400 |

Dear Shikha, The analyses are intended to produce different summaries, so you should not expect OLS to give the same result as a quantile regression. In general, the answer to your question is No. It may help to recall that the definition of the regression of Y on X (when x is a single "continuous" predictor) is the mean of the distribution of Y at each value of X, formally E(Y|X =x). The definition does not require that this function of x be a straight line, though that is often a good approximation. Similarly, with several "continuous" predictors, the regression of Y on those predictors is the mean of the distribution of Y at each combination of predictor values: E(Y|X1 = x1, X2 = x2, ...). For the .50 quantile, the summary you are fitting is the conditional median, as a function of the predictors. In general it differs from the conditional mean (i.e., the OLS regression). When you form the quartiles of Y and summarize by OLS, the fit is the conditional mean of the distribution of Y in the particular quartile. I hope this helps. David Hoaglin On Tue, May 28, 2013 at 10:11 PM, Shikha Sinha <shikha.sinha414@gmail.com> wrote: > I want to estimate a quantile regression at four quantiles (0.25 0.50 0.75 > 0.90). I used -sqreg command in stata. However, I was trying another > method, i.e. divide the sample into four quartiles based on distribution of > dependent variable (weightGRAM) and run a simple OLS for each quartile. The > results are shown below in 2. The OLS results are very different from > -sqreg results. > > Can someone explain me the difference between 1 and 2, and is there way to > replciate results in 1 by running an OLS model? > > > 1. sqreg weightGRAM member child_age , quantile(.25 .50 .75 .90) nolog > > 2. xtile qweight=weightGRAM,nq(4) > > . ta qweight > > 4 quantiles > of > weightGRAM Freq. Percent Cum. > > 1 2,738 25.13 25.13 > 2 2,778 25.49 50.62 > 3 2,730 25.05 75.67 > 4 2,651 24.33 100.00 > > Total 10,897 100.00 > > . bys qweight: reg weightGRAM member child_age * * 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 vs Quartile regression***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Quantile vs Quartile regression***From:*Nick Cox <njcoxstata@gmail.com>

**References**:**st: Quantile vs Quartile regression***From:*Shikha Sinha <shikha.sinha414@gmail.com>

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