论文标题
与网络溢出的线性二元数据模型的推断
Inference in Linear Dyadic Data Models with Network Spillovers
论文作者
论文摘要
当使用二元数据（即，由成对单位索引的数据）时，研究人员通常假设一个线性模型，使用普通最小二乘正方形进行估算，并使用``dydadic-bobust估算器''差异估计器进行推理。后者假设二元组是不相关的，如果它们不共享一个常见的单位（例如，如果这些单位不相同），那么这些单位不存在，我们的数据不存在。由于网络连接可能存在，因此可以在此类情况下，在我们的估计统计中允许使用效果，因此，由于网络连接而存在间接链接。当他们将邻居位于欧洲议会中时，投票行为。
When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares and conduct inference using ``dyadic-robust" variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not present in both pairs of data). We show that this assumption does not hold in many empirical applications because indirect links may exist due to network connections, generating correlated outcomes. Hence, ``dyadic-robust'' estimators can be biased in such situations. We develop a consistent variance estimator for such contexts by leveraging results in network statistics. Our estimator has good finite sample properties in simulations, while allowing for decay in spillover effects. We illustrate our message with an application to politicians' voting behavior when they are seating neighbors in the European Parliament.
