Date of this Version
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.
This article has been published in a revised form in Political Analysis [https://doi.org/10.1093/pan/mpv018]. This version is free to view and download for private research and study only. Not for re-distribution, re-sale or use in derivative works. © Cambridge University Press.
cluster robust variance estimation, dyadic data, agnostic regression
Aronow, P. M., Samii, C., & Assenova, V. A. (2015). Cluster-Robust Variance Estimation for Dyadic Data. Political Analysis, 23 (4), 564-577. http://dx.doi.org/10.1093/pan/mpv018
Date Posted: 19 February 2018
This document has been peer reviewed.