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From | Richard Goldstein <richgold@ix.netcom.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: Re: rank regression |
Date | Mon, 24 Feb 2014 13:37:47 -0500 |
so, given what you say they did, just use the -egen- command with the rank function (see the help file as there are options here) to form the new variables and then estimate with -regress- Rich On 2/24/14, 1:31 PM, R Zhang wrote: > Thank you for the reference and coding very much, Joseph ! > > I read the finance paper again, their regression model is in the form > of y=x1, x2, x3 etc., and the authors state that they replace both the > dependent variable and independent variables by their respective ranks > and evaluation the regression using the ordinary least squares. The > regression results table did not reveal what kind of tests they > conducted. > > table 4 of this article (not sure if you have subscription to it > http://onlinelibrary.wiley.com/doi/10.1111/1475-679X.00048/pdf > > page 304 4.2 is where they state rank regression > > > On Sun, Feb 23, 2014 at 10:25 PM, Joseph Coveney <stajc2@gmail.com> wrote: >> Rochelle Zhang wrote: >> >> a finance paper I was reading today uses rank regression , the author >> states that they replace both the dependent variable and independent >> variables by their respective ranks and evaluation the regression >> using the ordinary least squares. >> >> I searched "stata rank regression", and did not find anything. If you >> have knowledge how to conduct such regression, please share. >> >> -------------------------------------------------------------------------------- >> >> From your description, it sounds like the authors of the finance paper were just computing Spearman's correlation coefficient. See the Spearman section of the do-file's output below. >> >> On the other hand, if there were two (or more) independent variables, then they might have been doing what I call "Koch's nonparametric ANCOVA". See the last section of the output below. You can read about it at this URL: https://circ.ahajournals.org/content/114/23/2528.full and the references cited there. Scroll down until you come to the section that is titled, "Extensions of the Rank Sum Test". >> >> Joseph Coveney >> >> . clear * >> >> . set more off >> >> . set seed `=date("2014-02-24", "YMD")' >> >> . quietly set obs 10 >> >> . generate byte group = mod(_n, 2) >> >> . generate double a = rnormal() >> >> . generate double b = rnormal() >> >> . >> . * >> . * Spearman's rho >> . * >> . egen double ar = rank(a) >> >> . egen double br = rank(b) >> >> . regress ar c.br >> >> Source | SS df MS Number of obs = 10 >> -------------+------------------------------ F( 1, 8) = 0.64 >> Model | 6.13636364 1 6.13636364 Prob > F = 0.4458 >> Residual | 76.3636364 8 9.54545455 R-squared = 0.0744 >> -------------+------------------------------ Adj R-squared = -0.0413 >> Total | 82.5 9 9.16666667 Root MSE = 3.0896 >> >> ------------------------------------------------------------------------------ >> ar | Coef. Std. Err. t P>|t| [95% Conf. Interval] >> -------------+---------------------------------------------------------------- >> br | .2727273 .3401507 0.80 0.446 -.5116616 1.057116 >> _cons | 4 2.110579 1.90 0.095 -.8670049 8.867005 >> ------------------------------------------------------------------------------ >> >> . test br >> >> ( 1) br = 0 >> >> F( 1, 8) = 0.64 >> Prob > F = 0.4458 >> >> . // or >> . spearman a b >> >> Number of obs = 10 >> Spearman's rho = 0.2727 >> >> Test of Ho: a and b are independent >> Prob > |t| = 0.4458 >> >> . >> . * >> . * Koch's nonparametric ANCOVA >> . * >> . predict double residuals, residuals >> >> . ttest residuals, by(group) >> >> Two-sample t test with equal variances >> ------------------------------------------------------------------------------ >> Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] >> ---------+-------------------------------------------------------------------- >> 0 | 5 1.018182 1.601497 3.581057 -3.428287 5.464651 >> 1 | 5 -1.018182 .8573455 1.917083 -3.398555 1.362191 >> ---------+-------------------------------------------------------------------- >> combined | 10 0 .9211324 2.912876 -2.083746 2.083746 >> ---------+-------------------------------------------------------------------- >> diff | 2.036364 1.816545 -2.152596 6.225323 >> ------------------------------------------------------------------------------ >> diff = mean(0) - mean(1) t = 1.1210 >> Ho: diff = 0 degrees of freedom = 8 >> >> Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 >> Pr(T < t) = 0.8526 Pr(|T| > |t|) = 0.2948 Pr(T > t) = 0.1474 >> >> . // or >> . pwcorr residuals group, sig >> >> | residu~s group >> -------------+------------------ >> residuals | 1.0000 >> | >> | >> group | -0.3685 1.0000 >> | 0.2948 >> | >> >> . >> . exit >> >> end of do-file * * 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/