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Re: st: sigma u listed as zero despite unit variance


From   "Amber E. Boydstun" <aboydstun@psu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: sigma u listed as zero despite unit variance
Date   Tue, 24 Feb 2004 11:15:25 -0500 (EST)

Okay.  Thanks though!

- Amber

On Tue, 24 Feb 2004 16:08:43 +0000, "Alfonso Miranda Caso Luengo" wrote:

> Ignore my last post. In this case there is no such transformation.  
> 
> ===========================================
> Alfonso Miranda
> PhD Student
> 
> Economics Department
> University of Warwick
> Coventry CV4 7AL
> E-mail: Alfonso.Miranda-Caso-Luengo@warwick.ac.uk
> ===========================================
> >>> Alfonso.Miranda-Caso-Luengo@warwick.ac.uk 02/24/04 16:03 PM >>>
> I guess sigma_u = log(sigma). In many cases Stata  estimates a function of an
auxiliary parameter instead of the parameter itself. This thick helps to keep
estimates whithin a set of admissible values and ease ML maximisation. In any
case, you could always recover the value of the parameter in the original scale
applying the inverse function. Standard errors may be obtained  using the delta
method.     
> 
> ===========================================
> Alfonso Miranda
> PhD Student
> 
> Economics Department
> University of Warwick
> Coventry CV4 7AL
> E-mail: Alfonso.Miranda-Caso-Luengo@warwick.ac.uk
> ===========================================
> >>> aboydstun@psu.edu 02/24/04 15:39 PM >>>
> I'm very confused about why the sigma_u listed in the output from a random
> effects model reports a value of zero, despite the fact that I have unit
> variance in my dataset (I promise).  I'm attaching the output below.  Could
> someone please tell me what this means?  Thanks!
> 
> 
> 
> 
> xtreg dppct educpct repubpct fundpct whitepct, re;
> 
> Random-effects GLS regression                   Number of obs      =       220
> Group variable (i) : cohort                     Number of groups   =        10
> 
> R-sq:  within  = 0.0577                         Obs per group: min =        22
>        between = 0.6344                                        avg =      22.0
>        overall = 0.0622                                        max =        22
> 
> Random effects u_i ~ Gaussian                   Wald chi2(4)       =     14.27
> corr(u_i, X)       = 0 (assumed)                Prob > chi2        =    0.0065
> 
> ------------------------------------------------------------------------------
>        dppct |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
>      educpct |   .0596706   .0526787     1.13   0.257    -.0435777    .1629189
>     repubpct |   .2223392   .1046384     2.12   0.034     .0172516    .4274267
>      fundpct |   .2698267   .1397987     1.93   0.054    -.0041737     .543827
>     whitepct |   .2623254   .1236531     2.12   0.034     .0199697    .5046811
>        _cons |   .2656249   .1358642     1.96   0.051     -.000664    .5319138
> -------------+----------------------------------------------------------------
>      sigma_u |          0
>      sigma_e |  .09882399
>          rho |          0   (fraction of variance due to u_i)
> ------------------------------------------------------------------------------
> Amber E. Boydstun
> Graduate Student
> Department of Political Science
> Penn State University
> University Park, PA 16802-6200
> aboydstun@psu.edu
> *
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> 
> 
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> 

Amber E. Boydstun
Graduate Student
Department of Political Science
Penn State University
University Park, PA 16802-6200
aboydstun@psu.edu
*
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