Nonstandard Errors

Albert J. Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Kirchler, Sebastian Neususs, Michael Razen, Utz Weitzel, David Abad-Diaz, Meni Abudy, Tobias Adrian, Yacine Ait-Sahalia, Olivier Akmansoy, Jamie T. Alcock, Vitali Alexeev, Arash Aloosh, Livia Amato, Diego Amaya, James J. AngelAlejandro T. Avetikian, Amadeus Bach, Edwin Baidoo, Gaetan Bakalli, Li Bao, Andrea Barbon, Oksana Bashchenko, Parampreet C. Bindra, Geir H. Bjønnes, Jeffrey R. Black, Bernhard S. Black, Dimitar Bogoev, Santiago Bohorquez Correa, Oleg Bondarenko, Charles S. Bos, Ciril Bosch-Rosa, Elie Bouri, Christian Brownlees, Anna Calamia, Viet Nga Cao, Gunther Capelle-Blancard, Laura M. Capera Romero, Francesco Mazzola, Mathijs van Dijk, Patrick Verwijmeren, Sebastian Vogel, Wolf Wagner, Michel van der Wel, Antti Yang, Chen Zhou

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Scopus)

Abstract

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidencegenerating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
Original languageEnglish
Pages (from-to)2339-2390
Number of pages52
JournalJournal of Finance
Volume79
Issue number3
Early online date17 Apr 2024
DOIs
Publication statusPublished - Jun 2024

Bibliographical note

JEL Classification: C12, C18, G1, G14

© 2024 The Authors. The Journal of Finance published by Wiley Periodicals LLC on behalf of American Finance Association.

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