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

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication data for: Windows of Opportunity: Window Subseries Empirical Variance Estimators in International Relations
 
Identifier https://doi.org/10.7910/DVN/CU4EGC
 
Creator Patrick Heagerty
Michael D. Ward
Kristian Skrede Gleditsch
 
Publisher Harvard Dataverse
 
Description 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.
 
Date 2002