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Re: st: Categorical dependent variables and large dummy variable datasets


From   David Jacobs <jacobs.184@osu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Categorical dependent variables and large dummy variable datasets
Date   Thu, 19 Dec 2002 14:18:24 -0500

Perhaps I'm missing something important in your question, but doesn't the cluster option for the estimation procedures you wish to use suffice? For example, after an ologit or mlogit equation, simply put a clause ", cluster(cluster ID #)" with the parenthetic expression containing the variable name for the id number of your clusters. By using the cluster option in Stata, you automatically get Huber/White robust standard errors. You might also look at the survey estimators in the manuals.

Dave Jacobs

At 10:22 AM 12/18/2002 -0500, you wrote:

I work with cluster sampled survey data and estimate models with mainly
ordinal and nominal level dependent variables. I understand that the
standard errors in these models are affected by the fact that the
observations are not truly independent (i.e. grouped by clusters). I
cannot account for this simply by adding dummy variables in the model
because I have approximately 400 clusters.

What is the best way to handle this? If I were using OLS to estimate the
models; areg, absorb(cluster) would seem to be the way to go. However, I
need to use variants of logistic regression (mlogit, ologit).

I have also looked into using the ,robust option to calculate robust
standard errors. Will this provide correct standard error estimates?

Thank you.



Tom Brewer
Department of Justice Studies
Kent State University
113 Bowman Hall
Kent, OH 44242
330-672-0315

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