2026 General Seminar no. 9 - Yuya Sasaki (Vanderbilt)

Title: Genuinely Robust Inference for Clustered Data

by Harold D. Chiang, Yuya Sasaki, and Yulong Wang

Abstract: Conventional methods for cluster-robust inference are inconsistent when clusters of unignorably large size are present. We formalize this issue by deriving a necessary and sufficient condition for consistency, a condition frequently violated in empirical studies. Specifically, 77% of empirical research articles published in American Economic Review and Econometrica during 2020–2021 do not satisfy this condition. To address this limitation, we propose a new approach based on m-out-of-n bootstrap and establish its size control across broad classes of data-generating processes where conventional methods fail. Extensive simulation studies support our findings, demonstrating the reliability and effectiveness of the proposed approaches.