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Re: st: sigma_u = 0 in xtreg, re
From
John Antonakis <[email protected]>
To
[email protected]
Subject
Re: st: sigma_u = 0 in xtreg, re
Date
Mon, 29 Aug 2011 21:31:14 +0200
Hi:
You should visit what rho or ICC--intraclass correlation coefficient (in
ANOVA speak) means. From the ANOVA perspective, here's one way to
calculate it--check the Stata manual to see how it is precisely done in
loneway):
ICC1 = (MSb - MSw)/(MSb + ([k-1]*MSw)
Where
MSb = mean-square between
MSw=means-square within
k=average group size
Here's an example (from the help file):
. webuse auto7
. loneway mpg manufacturer_grp
This gives:
One-way Analysis of Variance for mpg: Mileage (mpg)
Number of obs = 74
R-squared = 0.5507
Source SS df MS F Prob > F
-------------------------------------------------------------------------
Between manufactur~p 1345.588 22 61.163092 2.84 0.0011
Within manufactur~p 1097.8714 51 21.526891
-------------------------------------------------------------------------
Total 2443.4595 73 33.472047
Intraclass Asy.
correlation S.E. [95% Conf. Interval]
------------------------------------------------
0.36827 0.13679 0.10017 0.63636
Estimated SD of manufactur~p effect 3.542478
Estimated SD within manufactur~p 4.639708
Est. reliability of a manufactur~p mean 0.64804
(evaluated at n=3.16)
Calculating ICC manually:
. dis ( 61.1630923 - 21.5268908)/( 61.1630923 + ((3.16-1)*21.5268908))
Gives:
.36815687
As for your data, it seems that you have a lot of within-cluster
variability (that is much higher than between-group variability). This
suggests that observations are pretty much "independent" (and once you
see the formula for ICC, it is obvious that it will approach zero as the
denominator becomes larger, ceteris paribus).
Try running the following and see what you get:
loneway y ID
You should get an ICC (intraclass correlation) that is zero.
If so, I would just estimate the following (and just to be sure that the
SEs are consistent):
reg y x, cluster(ID)
HTH,
John.
__________________________________________
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 20:45, Lloyd Dumont 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/
__________________________________________
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 20:45, Lloyd Dumont 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/