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| From | John Antonakis <John.Antonakis@unil.ch> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Re: rank regression |
| Date | Mon, 24 Feb 2014 08:22:16 +0100 |
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 08:05, John Antonakis 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/