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Windows of Opportunity: Window Subseries Empirical Variance Estimators in International Relations

Published online by Cambridge University Press:  04 January 2017

Patrick Heagerty ,
Michael D. Ward  and
Kristian Skrede Gleditsch
Patrick Heagerty
Affiliation:
Department of Biostatistics, University of Washington, Seattle, WA 98195. e-mail: heagerty@u.washington.edu
Michael D. Ward
Affiliation:
Department of Political Science and Center for Statistics in the Social Sciences, University of Washington, Seattle, WA 98195, and Éspace Éurope, Université Pierre Mendès France, Grenoble, France, BP 38040. e-mail: mdw@u.washington.edu
Kristian Skrede Gleditsch
Affiliation:
Department of Political Science, University of California, San Diego, La Jolla, CA 92093-0521. e-mail: kgleditsch@ucsd.edu
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Abstract

We show that temporal, spatial, and dyadic dependencies among observations complicate the estimation of covariance structures in panel databases. Ignoring these dependencies results in covariance estimates that are often too small and inferences that may be more confident about empirical patterns than is justified by the data. In this article, we detail the development of a nonparametric approach, window subseries empirical variance estimators (WSEV), that can more fully capture the impact of these dependencies on the covariance structure. We illustrate this approach in a simulation as well as with a statistical model of international conflict similar to many applications in the international relations literature.

Type
Research Article
Information
Political Analysis , Volume 10 , Issue 3: Special Issue on Spatial Methods in Political Science , Summer 2002 , pp. 304 - 317
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2002 

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