Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2024
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 evidence-generating 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.
Keywords
non-standard errors, multi-analyst approach, liquidity
Discipline
Finance and Financial Management | Management Sciences and Quantitative Methods
Research Areas
Finance
Publication
Journal of Finance
Volume
79
Issue
3
First Page
2339
Last Page
2390
ISSN
0022-1082
Identifier
10.1111/jofi.13337
Publisher
Wiley
Citation
MENKVELT, Albert J.; DREBER, Anna; et al.; YUESHEN, Bart Zhou; and PAGNOTTA, Emiliano Sebastian.
Non-standard errors. (2024). Journal of Finance. 79, (3), 2339-2390.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7633
Copyright Owner and License
Authors CC-BY
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Additional URL
https://doi.org/10.1111/jofi.13337
Included in
Finance and Financial Management Commons, Management Sciences and Quantitative Methods Commons