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

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.

本文证明，观测值间的时间、空间及二元相依性会使面板数据库中的协方差结构估计工作变得复杂。若忽略此类相依性，得到的协方差估计值往往偏小，且对经验模式的统计推断可能会超出数据所能支撑的置信水平。本文详细阐述了一种非参数方法——窗口子序列经验方差估计器（window subseries empirical variance estimators, WSEV）的开发流程，该方法能够更充分地捕捉上述相依性对协方差结构的影响。我们通过模拟实验，以及一个与国际关系领域文献中诸多应用场景类似的国际冲突统计模型，对该方法进行了实例演示与验证。