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Cluster–Robust Variance Estimation for Dyadic Data

Published online by Cambridge University Press:  04 January 2017

Peter M. Aronow ,
Cyrus Samii  and
Valentina A. Assenova
Peter M. Aronow
Affiliation:
Department of Political Science, Yale University, 77 Prospect Street, New Haven, CT 06520, e-mail: [email protected]
Cyrus Samii*
Affiliation:
Department of Politics, New York University, 19 West 4th Street, New York, NY 10012
Valentina A. Assenova
Affiliation:
School of Management, Yale University, 165 Whitney Avenue, New Haven, CT 06520, e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)
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Abstract

Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a non-parametric, sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enumerate conditions for estimator consistency. We also extend our results to repeated and weighted observations, including directed dyads and longitudinal data, and provide an implementation for generalized linear models such as logistic regression. We examine empirical performance with simulations and an application to interstate disputes.

Type
Letter
Information
Political Analysis , Volume 23 , Issue 4 , Autumn 2015 , pp. 564 - 577
Copyright
Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: The authors thank Neal Beck, Allison Carnegie, Dean Eckles, Donald Lee, Winston Lin, Kelly Rader, Olav Sorenson, the Political Analysis editors, and two reviewers for helpful comments. They thank Jonathan Baron and Lauren Pinson for research assistance. Supplementary materials for this article are available on the Political Analysis Web site. Replication materials are available on the Political Analysis Dataverse (https://dataverse.harvard.edu/dataverse/pan).

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