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

Jared Jennings, Jung Min Kim, Joshua Lee, Daniel Taylor*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)959-995
Number of pages37
JournalReview of Accounting Studies
Volume29
Issue number2
DOIs
StatePublished - Jun 2024

Keywords

  • Accounting research
  • C18
  • Causal models
  • Fixed effects
  • G17
  • Measurement error

ASJC Scopus subject areas

  • Accounting
  • General Business, Management and Accounting

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