Speaker:Yulong Wang, Associate Professor (Lehigh University, USA)

Host: Cong Xie, Assistant Professor (China Institute of Economic Research, Liaoning University)

Guest:Xiangjun Ma, Professor (China Institute of Economic Research, Liaoning University)

Time:14:00–15:30 (Beijing Time), Thursday, May 21, 2026

Location:Room 112, Economics Building, Puhe Campus, Liaoning University

Online Access:Tencent Meeting ID: 377-781-126

Language:Chinese / English

Abstract:

Conventional cluster-robust inference can be invalid when data contain clusters of unignorably large size. We formalize this issue by deriving a necessary and sufficient condition for its validity, and show that this condition is frequently violated in practice: specifications from 77% of empirical research articles in 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 new 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.

Speaker Profile:

Yulong Wang is an Associate Professor at Lehigh University, USA. He holds a Ph.D. in Economics from Princeton University. He serves as an Associate Editor for the journal Econometric Reviews. His primary research interests include theoretical and applied econometrics, statistics, international trade, and health economics. His research has been published in top-tier academic journals such as the Journal of the American Statistical Association, Journal of Econometrics, Journal of Business & Economic Statistics, Econometric Theory, Journal of Applied Econometrics, and Journal of Health Economics.