Do t-Statistic Hurdles Need to be Raised?

Fuente: arXiv
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Autor principal: Chen, Andrew Y.
Formato: Preprint
Publicado: 2022
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author Chen, Andrew Y.
author_facet Chen, Andrew Y.
contents 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 FDR) can be strongly identified, as data on published findings is plentiful. I demonstrate these results theoretically and in an empirical analysis of the cross-sectional return predictability literature.
format Preprint
id arxiv_https___arxiv_org_abs_2204_10275
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Do t-Statistic Hurdles Need to be Raised?
Chen, Andrew Y.
General Finance
Econometrics
Statistical Finance
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 FDR) can be strongly identified, as data on published findings is plentiful. I demonstrate these results theoretically and in an empirical analysis of the cross-sectional return predictability literature.
title Do t-Statistic Hurdles Need to be Raised?
topic General Finance
Econometrics
Statistical Finance
url https://arxiv.org/abs/2204.10275