Genuinely Robust Inference for Clustered Data

Harold D. Chiang, Yuya Sasaki and Yulong Wang

Papers from arXiv.org

Abstract: Conventional cluster-robust inference can be inconsistent when data contain clusters of unignorably large size. We formalize this issue by deriving a necessary and sufficient condition for the consistency, and show that this condition is frequently violated in practice: 77% of empirical research articles published in the American Economic Review and Econometrica during 2020-2021 appear not to meet it. To address this limitation, we propose a genuinely robust inference procedure based on a novel cluster score bootstrap. We establish its validity and size control across broad classes of data-generating processes where conventional methods break down. Simulation studies corroborate our theoretical findings, and empirical applications illustrate that employing the proposed method can substantially alter conventional statistical conclusions.

Date: 2023-08, Revised 2025-09
New Economics Papers: this item is included in nep-ecm
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