
From  "Huseyin Tastan" <hntastan@hotmail.com> 
To  statalist@hsphsun2.harvard.edu 
Subject  Re: st: three level variance components using xtmixed 
Date  Wed, 16 Aug 2006 06:40:23 0400 
From: Joseph Coveney <jcoveney@bigplanet.com>
ReplyTo: statalist@hsphsun2.harvard.edu
To: Statalist <statalist@hsphsun2.harvard.edu>
Subject: Re: st: three level variance components using xtmixed
Date: Wed, 16 Aug 2006 19:23:36 +0900
Huseyin Tastan wrote:
I want to analyze variance components of a measure of firm performance (such
as return on equity) using random effects at three levels: industry level,
firm level and time level.
I have data on
industries: i=1,2,...,20 (there are 20 industries)
firms: j=1,2,...,1000 (there are 1000 firms)
and
years: t=1,2,...,10 (there are 10 years)
The specific model is written as follows:
y_ijt = a + b_i + c_j + d_t + e_ijt
There are four variances to estimate in this model:
var(y) = var(b) + var(c) + var(d) + var(e)
I have tried xtmixed to estimate this model but the convergence was
extremely slow (I used reml option). And for some dependent variables it
didnt even converge. The command I used was something like this:
xtmixed y  industry:  firm:  year:, variance
[excerpted]

From the description, it looks as if there are only two variance components
above the residuals.
y_ijt = mu + industry_i + firm_ij + e_ijt
Try xtmixed y  industry:  firm:, variance.
Do you have multiple measurements per year? Is there a reason to consider
time as random?
Joseph Coveney
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