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.

我们证明，观测值间的时间相依性、空间相依性与二元相依性，会使得面板数据集的协方差结构估计更为复杂。若忽略此类相依性，得到的协方差估计值往往偏小，且针对经验模式的统计推断，其置信水平可能超出数据所支撑的合理范围。本文详述了非参数方法窗口子序列经验方差估计器（window subseries empirical variance estimators，WSEV）的研发过程，该方法能够更充分地捕捉此类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诸多应用场景相似的国际冲突统计模型，对该方法进行了演示验证。