Genuinely Robust Inference for Clustered Data

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Tác giả: Harold D Chiang, Yuya Sasaki, Yulong Wang

Ngôn ngữ: eng

Ký hiệu phân loại: 523.85 Clusters

Thông tin xuất bản: 2023

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 198084

Comment: This paper supersedes the manuscripts previously circulated under the titles "On the Inconsistency of Cluster-Robust Inference and How Subsampling Can Fix It" and "Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method" (arXiv:2210.16991)Conventional cluster-robust inference methods are inconsistent when clusters are unignorably large. We derive a necessary and sufficient condition for consistency, which is violated in 77% of empirical studies published in American Economic Review and Econometrica (2020-2021). To address this, we propose two methods: (i) score subsampling, which retains the original estimator, and (ii) size-adjusted reweighting, which is easy to implement in software like Stata and remains valid if the cluster size follows Zipf's law. Simulations confirm the reliability and uniform size control of these approaches, offering robust alternatives where conventional methods fail.
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