Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: sigma_u = 0 in xtreg, re


From   John Antonakis <John.Antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: sigma_u = 0 in xtreg, re
Date   Tue, 30 Aug 2011 08:18:51 +0200

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/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index