Measurement error, fixed effects, and false positives in accounting research

We show theoretically and empirically that measurement error can bias in favor of falsely rejecting a true null hypothesis (i.e., a “false positive”) and that regression models with high-dimensional fixed effects can exacerbate measurement error bias and increase the likelihood of false positives. W...

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Bibliographic Details
Published in:Review of accounting studies Vol. 29; no. 2; pp. 959 - 995
Main Authors: Jennings, Jared, Kim, Jung Min, Lee, Joshua, Taylor, Daniel
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2024
Springer Nature B.V
Subjects:
Accounting
Accounting/Auditing
Bias
Business and Management
Corporate Finance
Econometrics
Measurement errors
Public Finance
Rates of return
Researchers
Simulation
Fixed effects
C18
Accounting research
G17
Measurement error
Causal models
ISSN:1380-6653, 1573-7136
Online Access:Get full text
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Summary:We show theoretically and empirically that measurement error can bias in favor of falsely rejecting a true null hypothesis (i.e., a “false positive”) and that regression models with high-dimensional fixed effects can exacerbate measurement error bias and increase the likelihood of false positives. We replicate inferences from prior work in a setting where we can directly observe the amount of measurement error and show that the combination of measurement error and fixed effects materially inflates coefficients and distorts inferences. We provide researchers with a simple diagnostic tool to assess the possibility that the combination of measurement error and fixed effects might give rise to a false positive, and encourage researchers to triangulate inferences across multiple empirical proxies and multiple fixed effect structures.
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ISSN:1380-6653
1573-7136
DOI:10.1007/s11142-023-09754-z