Cluster-Robust Variance Estimation for Dyadic Data
Cluster-Robust Variance Estimation for Dyadic Data
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DOI：
10.1093/pan/mpv018
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发表时间：
2015-09-01
影响因子：
5.4
通讯作者：
Assenova, Valentina A.
中科院分区：
文献类型：
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作者：
Aronow, Peter M.;Samii, Cyrus;Assenova, Valentina A.
二元数据在社会科学中很常见，尽管此类设置的推理涉及复杂的聚类结构。社会科学中的许多分析未能解释多个二元组共享一个成员的事实，因此错误可能在这些二元组之间相关。我们提出了一种用于线性回归的非参数、三明治型鲁棒方差估计器，以解释二元数据中的这种聚类。我们列举了估计器一致性的条件。我们还将结果扩展到重复和加权观察，包括有向二元组和纵向数据，并提供逻辑回归等广义线性模型的实现。我们通过模拟和在州际争端中的应用来检验实证表现。
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.