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Re: st: Large differences between standard errors with and without clustering

From   Maarten buis <>
Subject   Re: st: Large differences between standard errors with and without clustering
Date   Tue, 30 Oct 2007 16:46:36 +0000 (GMT)

--- "M.C.D. van Damme" <> wrote:
> I am estimating an OLS regression with variables on the individual
> and country level, correcting for intragroup correlation (,cl
> (country)). I have 13 countries. 
> After correction, not only the standard error of the country level
> variable is corrected, but also the standard errors of some of the
> individual level variables are much higher. Normally, I do not find
> such big differences.
> What can be the reason for these large adjustments in standard errors
> on the individual level?
> In other words, what is the cluster option exactly doing?

Normally standard errors are based on the assumption that you have
drawn a simple random sample. The cluster option corrects the standard
errors (but not the coefficients) when your sample isn't random but
contains clusters. Robust standard errors (including -cluster-) are
discussed in the User's guide (for Stata 10) chapter 20.15. It was also
discussed in the User's guide of previous version but my Stata 9
manuals now reside in the office of my advisor, who is gone now, so I
can't give you the chapter.

If you are worried by this try estimating your model with -xtreg- or 
-xtmixed- with the -re- option. 13 countries is not a lot, so you can't
enter a lot of country level variables, but that is as it should be: on
the country level you have only 13 observations, so more than 1 or 2
country level variables doesn't make sense in any model even if Stata
gives you results.

Hope this helps,

Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

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