Do t-Statistic Hurdles Need to Be Raised?

• 成果类型: Article
• 署名作者: Chen, Andrew Y.
• 署名单位: Federal Reserve System - USA; Federal Reserve System Board of Governors
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.03083
• 发表日期: 2025
• 页码: 5830-5848
• 关键词: Stock market predictability; Stock market anomalies; p -hacking; Multiple Testing

摘要

Many scholars have called for raising statistical hurdles to guard against false discoveries in academic publications. I show these calls may be difficult to justify empirically. Published data exhibit bias: Results that fail to meet existing hurdles are often unobserved. These unobserved results must be extrapolated, which can lead to weak identification of revised hurdles. In contrast, statistics that can target only published findings (e.g. empirical Bayes shrinkage and the false discovery rate) can be strongly identified, as data on published findings are plentiful. I demonstrate these results theoretically and in an empirical analysis of the cross-sectional return predictability literature.