Re: st: Re: random effects panel model - interpretation of rho=0

["Martin Weiss" <martin.weiss1@gmx.de>]

<>

Very ture and my fault, conditioning on e(sample) only means the 
rows/observations used in estimation are summarized. To restrict to the 
covariates/columns, install Maarten`s -ssc d indeplist- and then, 
post-estimation:

***
indeplist
xtsum `r(X)' if e(sample)
***


HTH
Martin
_______________________
----- Original Message ----- 
From: "P K" <statistics_2009@yahoo.de>
To: <statalist@hsphsun2.harvard.edu>
Sent: Wednesday, May 20, 2009 10:20 PM
Subject: AW: st: Re: random effects panel model - interpretation of rho=0


> Hi Martin,
>
> it gives me the summary of most variables in my dataset, which are far 
> more than the ones used in the regression. Can I limit the command somehow 
> to
> the variables used in my estimation? ...otherwise I would have to create a 
> separate dataset for the variables used in the regression...
>
> thanks
>
>
>
> ----- Ursprüngliche Mail ----
> Von: Martin Weiss <martin.weiss1@gmx.de>
> An: statalist@hsphsun2.harvard.edu
> Gesendet: Mittwoch, den 20. Mai 2009, 14:01:43 Uhr
> Betreff: st: Re: random effects panel model - interpretation of rho=0
>
> <>
>
> What does -xtsum if e(sample)- after estimation give you?
>
>
> HTH
> Martin
> _______________________
> ----- Original Message ----- From: "P K" <statistics_2009@yahoo.de>
> To: <statalist@hsphsun2.harvard.edu>
> Sent: Wednesday, May 20, 2009 7:52 PM
> Subject: st: random effects panel model - interpretation of rho=0
>
>
> > Hi,
> >
> > I run a random effects panel model of 64 subjects for 10 years each and 
> > have a question concerning the results: My output tells me that
> > 0% of the variance of the dependent variable is between subjects and 100% 
> > is within subjects (rho).
> >
> > I am not sure whether I understand the interpretation correctly, but I 
> > find this result a bit uncommon given that I test for a random effects 
> > model and
> > my theory predicts a lot of variation between my subjects.
> >
> > Could there by any mistake in the analysis?
> > Thanks,
> > Pat
> >
> > Here's the STATA output:
> >
> > Random-effects GLS regression                   Number of obs      = 545
> > Group variable: number                          Number of groups   = 64
> >
> > R-sq:  within  = 0.0138                         Obs per group: min = 2
> >       between = 0.1741                                        avg = 8.5
> >       overall = 0.0168                                        max = 10
> >
> > Random effects u_i ~ Gaussian                   Wald chi2(7)       = 9.16
> > corr(u_i, X)       = 0 (assumed)                Prob > chi2        = 
> > 0.2416
> >
> > ------------------------------------------------------------------------------
> > AverCh~2adj2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. 
> > Interval]
> > -------------+----------------------------------------------------------------
> > duration_c~e |  -.9013214   .3550697    -2.54 
> > 011    -1.597245   -.2053976
> >   sicchange |   2.527742   4.073774     0.62   0.535    -5.456709 
> > 10.51219
> >       ukdum |   .5491039   1.126746     0.49   0.626    -1.659277 
> > 2.757485
> >     frendum |  -1.780095   1.522111    -1.17   0.242    -4.763378 
> > 1.203189
> > regnlistdum |  -.6389011    1.09439    -0.58   0.559    -2.783865 
> > 1.506063
> >      mutdum |   .9720512   1.790017     0.54   0.587    -2.536318 4.48042
> > lgcount_st~y |  -.2016355   .6181168    -0.33   0.744    -1.413122 
> > 1.009851
> >       _cons |   1.249498   1.361879     0.92   0.359    -1.419735 
> > 3.918731
> > -------------+----------------------------------------------------------------
> >     sigma_u |          0
> >     sigma_e |  11.774903
> >         rho |          0   (fraction of variance due to u_i)
> > ------------------------------------------------------------------------------
> >
> >
> >
> >
> > *
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