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From | Lloyd Dumont <lloyddumont@yahoo.com> |
To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
Subject | Re: st: sigma_u = 0 in xtreg, re |
Date | Wed, 31 Aug 2011 09:04:51 -0700 (PDT) |
Thank you all very, very much for pondering this for me. You have been extremely helpful. Lloyd ----- Original Message ----- From: John Antonakis <John.Antonakis@unil.ch> To: statalist@hsphsun2.harvard.edu Cc: Sent: Tuesday, August 30, 2011 2:18 AM Subject: Re: st: sigma_u = 0 in xtreg, re OK. Thus, Lloyed might as well use pooled OLS with cluster robust standard errors, right? Best, J. __________________________________________ Prof. John Antonakis Faculty of Business and Economics Department of Organizational Behavior 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 __________________________________________ On 30.08.2011 00:05, Schaffer, Mark E wrote: > I think it's true in finite samples as well. At least, that's how I read what Baltagi has to say about it in chap 2 of his textbook ("Econometric Analysis of Panel Data" - it's in the section on the random effects model). > > --Mark > >> -----Original Message----- >> From: owner-statalist@hsphsun2.harvard.edu >> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of >> Stas Kolenikov >> Sent: 29 August 2011 22:26 >> To: statalist@hsphsun2.harvard.edu >> Subject: Re: st: sigma_u = 0 in xtreg, re >> >> John, >> >> certainly so asymptotically when the true sigma_u = 0. >> Whether that is exactly true in finite samples, I don't know, >> although at the face of it, it looks reasonable: >> >> set seed 1234 >> set obs 100 >> gen id = _n >> gen ni = rpoisson(5) + 1 >> expand ni >> gen x = uniform() >> gen y = x + rnormal() >> xtreg y x, i(id) >> reg y x >> >> On Mon, Aug 29, 2011 at 4:14 PM, John Antonakis >> <John.Antonakis@unil.ch> wrote: >>> One clarification; when rho = 0 aren't these estimates >> simply OLS estimates? >>> Best, >>> J. >>> >>> __________________________________________ >>> >>> Prof. John Antonakis >>> Faculty of Business and Economics >>> Department of Organizational Behavior >>> 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 >>> __________________________________________ >>> >>> >>> On 29.08.2011 22:50, Stas Kolenikov wrote: >>>> Note that you have a very decent R^2, especially the >> between one. It >>>> looks, hence, that all of the bewteen-panel variability in Y is >>>> explained by the between-panel variability in X's (the ICC's were >>>> quite similar for each of the variables), so there indeed >> is little >>>> left that needs explaining. -xtsum- is somewhat misleading >> here, as >>>> this is a marginal measure, not a conditional one (which is what >>>> matters for the regression). >>>> >>>> Technically speaking, you are hitting a corner solution >> for sigma_u. >>>> In the simplest form of the estimator for sigma_u, it is formed as >>>> [mean total square] - [mean within square], so substraction of two >>>> non-negative quantities gave you a negative quantity (which was >>>> truncated upwards to zero). More elaborate estimators exist that >>>> guarantee both within and between sigmas to be positive, but for a >>>> vast majority of situations, the simple one should do just >> fine, so >>>> that's what -xtreg, re- does. >>>> >>>> On Mon, Aug 29, 2011 at 1:45 PM, Lloyd >> Dumont<lloyddumont@yahoo.com> >>>> wrote: >>>>> Hello, Statalist. >>>>> >>>>> I am a little confused by the output from an -xtreg, re- estimate. >>>>> >>>>> Basically, I end up with sigma_u = 0, which of course >> yields rho = 0. >>>>> That seems very odd to me. I would guess that that should only >>>>> happen if there is no between-subject variation. But, (I >> think) I >>>>> can tell from examining the data that that is not the case. >>>>> >>>>> I have tried to create a mini example... First, I will >> show the xtreg >>>>> results. Then, I will show you what I think is the evidence that >>>>> there really IS some between-subject variation. >>>>> >>>>> Am I missing something obvious here? Thank you for your help and >>>>> suggestions. Lloyd Dumont >>>>> >>>>> >>>>> . xtreg Y X, re >>>>> >>>>> Random-effects GLS regression Number of >> obs = >>>>> 3133 >>>>> Group variable: ID Number of >> groups = >>>>> 31 >>>>> >>>>> R-sq: within = 0.4333 Obs per >> group: min = >>>>> 1 >>>>> between = 0.8278 >> avg = >>>>> 101.1 >>>>> overall = 0.4579 >> max = >>>>> 124 >>>>> >>>>> Wald >> chi2(1) = >>>>> 2644.38 >>>>> corr(u_i, X) = 0 (assumed) Prob> >> chi2 = >>>>> 0.0000 >>>>> >>>>> >>>>> >> -------------------------------------------------------------------- >>>>> ---------- >>>>> Y | Coef. Std. Err. z P>|z| >> [95% Conf. >>>>> Interval] >>>>> >>>>> >> -------------+------------------------------------------------------ >>>>> -------------+---------- >>>>> X | -.0179105 .0003483 -51.42 0.000 -.0185932 >>>>> -.0172279 >>>>> _cons | 1.004496 .0017687 567.92 0.000 1.001029 >>>>> 1.007963 >>>>> >>>>> >> -------------+------------------------------------------------------ >>>>> -------------+---------- >>>>> sigma_u | 0 >>>>> sigma_e | .07457648 >>>>> rho | 0 (fraction of variance due to u_i) >>>>> >>>>> >> -------------------------------------------------------------------- >>>>> ---------- >>>>> >>>>> >>>>> >>>>> >>>>> . xtsum X >>>>> >>>>> Variable | Mean Std. Dev. Min Max | >>>>> Observations >>>>> >>>>> >> -----------------+--------------------------------------------+----- >> -----------------+--------------------------------------------+----- >> -----------------+--------------------------------------------+----- >>>>> -----------------+--------------------------------------------+- >>>>> X overall | 3.277883 3.875116 0 >> 42.5 | >>>>> N = >>>>> 3137 >>>>> between | 1.286754 0 >> 6.890338 | n >>>>> = >>>>> 31 >>>>> within | 3.729614 -3.612455 >> 42.24883 | T-bar >>>>> = >>>>> 101.194 >>>>> >>>>> >>>>> >>>>> . xtsum Y >>>>> >>>>> Variable | Mean Std. Dev. Min Max | >>>>> Observations >>>>> >>>>> >> -----------------+--------------------------------------------+----- >> -----------------+--------------------------------------------+----- >> -----------------+--------------------------------------------+----- >>>>> -----------------+--------------------------------------------+- >>>>> Y overall | .9457124 .1025887 0 >> 1 | >>>>> N = >>>>> 3133 >>>>> between | .0315032 .8387879 >> 1 | n >>>>> = >>>>> 31 >>>>> within | .0985757 -.0235858 >> 1.106925 | T-bar >>>>> = >>>>> 101.065 >>>>> >>>>> . >>>>> >>>>> >>>>> * >>>>> * For searches and help try: >>>>> * http://www.stata.com/help.cgi?search >>>>> * http://www.stata.com/support/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/statalist/faq >>> * http://www.ats.ucla.edu/stat/stata/ >>> >> >> >> -- >> Stas Kolenikov, also found at http://stas.kolenikov.name >> Small print: I use this email account for mailing lists only. >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/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/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/statalist/faq * http://www.ats.ucla.edu/stat/stata/