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st: regression with dyadic data, QAP or fixed effects?


From   Derek Darves <darves@uoregon.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: regression with dyadic data, QAP or fixed effects?
Date   Thu, 13 Oct 2005 13:35:08 -0700

Hi All,

I have a dataset that consists of all possible pairs of a group of ~500 companies (making ~118k dyads).

The variables in the dyadic dataset deal with common occurrences, e.g. a variable is set to one if both members of the dyad are members of the same organization. Other variables like sales that are continuous, and are equal to the geometric mean of the sales for each company in the dyad.

This is my question: obviously the SEs of of any regression with this data will be downward biased, since many of the errors should be correlated (e.g. between to dyads with a common subunit). Does anyone have any ideas about how best to adjust the SEs? I have not found an implementation of QAP within Stata, so I am wondering if there is some way to analyze this data within Stata, e.g. by using a dummy for each of the 500 companies that makes up a dyad, or using robust SE correction, etc.

The DV is binary, and is set to 1 if both companies share a certain attribute.
I have also computed the DV as a count of the number of common attributes b/w the two firms in a dyad. Thus, I am interested in either logit or nbreg estimation techniques. Umlike the IR literature which uses dyads of countries, I do not have multiple years of data, just a single cross-section of company dyads.

Any ideas on how to analyze this data would be greatly appreciated.

Derek
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