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Re: st: three level variance components using xtmixed


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

No, I do not have multiple measurements per year. The reason for considering time (year) is that it captures some macroeconomic events affecting all firms in all industries. I want to get how much of the variation in the dependent variable is due to firm, industry and time affects. I can run the model with two levels for each year and compare the results but I want to know if time effect is important or not.


From: Joseph Coveney <jcoveney@bigplanet.com>
Reply-To: 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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