Management Papers

Document Type

Journal Article

Date of this Version

2015

Publication Source

Political Analysis

Volume

23

Issue

4

Start Page

564

Last Page

577

DOI

10.1093/pan/mpv018

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.

Copyright/Permission Statement

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.

Comments

At the time of publication, author Valentina A. Assenova was affiliated with Yale University. Currently, she is a faculty member at the University of Pennsylvania.

Keywords

cluster robust variance estimation, dyadic data, agnostic regression

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Date Posted: 19 February 2018

This document has been peer reviewed.