Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
From | R Zhang <r05zhang@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: Re: rank regression |
Date | Mon, 24 Feb 2014 13:32:23 -0500 |
Hi John, what do you mean by rank ordering to be roughly equidistant? please excuse my ignorance. Rochelle On Mon, Feb 24, 2014 at 2:05 AM, John Antonakis <John.Antonakis@unil.ch> wrote: > If the dependent variable is a rank, where rank ordering does not seem to be > roughly equidistant, then they should have used an ordinal probit or logisit > estimator: -oprobit- or -ologisit-. If the independent variables are in the > same boat (non equidistant), I would model them as dummies. > > Best, > J. > > __________________________________________ > > John Antonakis > Professor of Organizational Behavior > Director, Ph.D. Program in Management > > Faculty of Business and Economics > University of Lausanne > Internef #618 > CH-1015 Lausanne-Dorigny > Switzerland > Tel ++41 (0)21 692-3438 > Fax ++41 (0)21 692-3305 > http://www.hec.unil.ch/people/jantonakis > > Associate Editor: > The Leadership Quarterly > Organizational Research Methods > __________________________________________ > > > On 24.02.2014 04:25, Joseph Coveney 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/ > > > * > * 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/