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From |
R Zhang <r05zhang@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Re: rank regression |

Date |
Mon, 24 Feb 2014 13:31:10 -0500 |

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/ * * 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: Re: rank regression***From:*Richard Goldstein <richgold@ix.netcom.com>

**References**:**st: rank regression***From:*R Zhang <r05zhang@gmail.com>

**st: Re: rank regression***From:*"Joseph Coveney" <stajc2@gmail.com>

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