Author:
Aronow Peter M.,Samii Cyrus,Assenova Valentina A.
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.
Publisher
Cambridge University Press (CUP)
Subject
Political Science and International Relations,Sociology and Political Science
Cited by
69 articles.
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