David,
Subject: st: Clustering vs. Indicator variables in a Logit
To: [email protected]
From: [email protected]
Date sent: Mon, 8 Nov 2004 10:59:45 -0500
Send reply to: [email protected]
> Hello All,
>
> I understand that clustering specifies that observations be independent
> across groups while allowing for changes in variance within a group. My
> question is how is this different than controlling for a group with an
> indicator (0/1) variables.
>
> For example, If my data contains patient data for 12 hospitals and my LHS
> variable is (0/1) for recovery and my RHS variables include characteristic
> variables, treatment type, etc. What is the difference between clustering
> on hospital or creating an indicator variable for each hospital?
The indicator variable approach is a particular functional form that
allows for intra-group correlation. With clustering, you allow for
arbitrary intra-group correlation. For example, if you were working
with panel data, an indicator variable approach would be like
assuming intra-group correlation that never dies out over time,
whereas the cluster approach allows for any time of serial
correlation within the group.
Also, as Dave pointed out, the clustering approach gives you robust
standard errors and doesn't affect the coefficients (unless you go
down a 2-step GMM route or something like that).
Last point - you can combine the two approaches, i.e., use indicator
variables in the estimation, and also request standard errors that
are robust to any intra-group correlation that the indicator variable
approach didn't properly account for.
Hope this helps.
--Mark
>
> Thanks in advance for any commentary,
>
> David J. Bernstein, Ph.D.
> [email protected]
>
>
>
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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
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