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Re: st: Re: rank regression


From   John Antonakis <[email protected]>
To   [email protected]
Subject   Re: st: Re: rank regression
Date   Mon, 24 Feb 2014 08:22:16 +0100

Funny....mistyped twice.....meant to say logistic and not "logisit". The command is -ologit-

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
>>
>>
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