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From |
"Buchs M." <M.Buechs@soton.ac.uk> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: How to compare coefficients from ols and quantile regressions |

Date |
Wed, 28 Nov 2012 17:38:41 +0000 |

Many thanks, Marteen, much appreciated! Milena -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten Buis Sent: 28 November 2012 09:55 To: statalist@hsphsun2.harvard.edu Subject: Re: st: How to compare coefficients from ols and quantile regressions On Wed, Nov 28, 2012 at 10:03 AM, Buchs M. wrote: > We are interested in comparing the coefficients (based on the question whether the association between household emissions and hh characteristics differs for mean and high emissions). Many coefficients do in fact differ but we'd like to test whether they are significantly different. It seems to me you can just reframe your question in terms of comparing the effect of variables on a measure of central tendency and the effects on the 75th percentile. Than you can use the median (50th percentile) instead of the mean as your measure of central tendency, and you can use -sqreg- to simultaneously estimate both regression and -test- to test whether the effects are equal. Not only is this easier to implement, but the to regression are conceptually similar, so are substantively much easier to compare. --- Jay Verkuilen wrote: > There's no particularly good reason that a QR for the third quartile > would be directly comparable to the OLS coefficients that I know of, As long as the errors are homoskedatic the effects of explanatory variables on any quantile should be the same as the effect on the mean. I'll agree that that is not a very substantively interesting relationship, but it is a relationship none the less. *--------------------- begin example ---------------------- set seed 123456 drop _all set obs 10000 gen x = rnormal() // homoscedastic errors gen y = 1 + .5*x + .5*rnormal() // see the effect in linear regression reg y x // the constant changes but the effect of x is the // same for all quantiles as the effect in -regress- sqreg y x , q(10 25 50 75 90) // heteroskedastic errors replace y = 1 + .5*x + (.5 + 1/8*x)*rnormal() // estimate regression, // point estimates are still consistent reg y x // see that errors are heteroskedastic rvfplot // see that the effects of x for the different // quantiles is different from the effect in -regress- sqreg y x , q(10 25 50 75 90) *---------------------- end example ----------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * 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/ * * 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/

**References**:**st: How to compare coefficients from ols and quantile regressions***From:*"Buchs M." <M.Buechs@soton.ac.uk>

**Re: st: How to compare coefficients from ols and quantile regressions***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**Re: st: How to compare coefficients from ols and quantile regressions***From:*Austin Nichols <austinnichols@gmail.com>

**RE: st: How to compare coefficients from ols and quantile regressions***From:*"Buchs M." <M.Buechs@soton.ac.uk>

**Re: st: How to compare coefficients from ols and quantile regressions***From:*Maarten Buis <maartenlbuis@gmail.com>

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