Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t -Statistic
研究了成对数据线性模型中，使用成对稳健方差估计量的t统计量在何种条件下渐近正态，并通过模拟验证正态近似的有效性及有限样本修正的表现，为应用研究者提供使用指南。
This article is concerned with inference in the linear model with dyadic data. Dyadic data are indexed by pairs of "units;" for example, trade data between pairs of countries. Because of the potential for observations with a unit in common to be correlated, standard inference procedures may not perform as expected. We establish a range of conditions under which a t-statistic with the dyadic-robust variance estimator of Fafchamps and Gubert is asymptotically normal. Using our theoretical results as a guide, we perform a simulation exercise to study the validity of the normal approximation, as well as the performance of a novel finite-sample correction. We conclude with guidelines for applied researchers wishing to use the dyadic-robust estimator for inference.