Cluster-Robust Inference Robust to Large Clusters

Abstract

The recent literature Sasaki and Wang (2022) points out that the conventional cluster-robust standard errors fail in the presence of large clusters. We propose a novel method of cluster-robust inference that is valid even in the presence of large clusters. Specifically, we derive the asymptotic distribution for the t-statistics based on the common cluster-robust variance estimators when the distribution of cluster sizes follows a power law with an exponent less than two. We then propose an inference procedure based on subsampling and show its validity. Our proposed method does not require tail index estimation and remains valid under the usual thin-tailed scenarios as well