Daniel,
In the case you describe, -cluster- will give you standard errors
that are correct even though the independence assumption is violated.
The coefficients are still the OLS coefficients, however.
On the other hand, if you estimate by ML or FGLS, you get different
coefficients because you use the tighter specification of the error
components to increase the efficiency of the estimation.
If you want a different take on it, the paper on IV/GMM that Kit
Baum, Steve Stillman and I did for the Stata Journal (3:1, 2003) has
a brief discussion that puts it in a GMM framework.
Hope this helps.
--Mark
To: [email protected]
From: Daniel Lawson <[email protected]>
Subject: st: reg, cluster()
Date sent: Tue, 14 Sep 2004 10:41:59 -0500
Send reply to: [email protected]
> Dear Statalist members,
>
> I have a question about the ``cluster'' option (common to many
> commands, but I am invoking it from the simple ``reg'' command).
>
> In a model where every individual i is also a part of a group j, and
> the dependent variable is determined by characteristics not only of the
> individual i but also of the group j, it seems like a regression ought
> to attempt to fit the model
>
> y_i = x_i b + w_j g + e_i + u_j
>
> where x_i are individual characteristics, w_j are group
> characteristics, e_i is an individual error term, and u_j is a group
> error term. Can anyone help me contrast an error component model like
> this (fitted either through f-GLS or ML) with what ``cluster'' does? I
> have already read William Rogers' article ``Regression standard errors
> in clustered samples'', but am still unclear.
>
> Thank you very much if anyone has any pointers on this.
>
> Peace,
> Daniel Lawson
>
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Prof. Mark E. Schaffer
Director
Centre for Economic Reform and Transformation
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS UK
44-131-451-3494 direct
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