Count (and count-like) data in finance

Jonathan B. Cohn, Zack Liu and Malcolm I. Wardlaw

Journal of Financial Economics, 2022, vol. 146, issue 2, 529-551

Abstract: This paper assesses different econometric approaches to working with count-based outcome variables and other outcomes with similar distributions, which are increasingly common in corporate finance applications. We demonstrate that the common practice of estimating linear regressions of the log of 1 plus the outcome produces estimates with no natural interpretation that can have the wrong sign in expectation. In contrast, a simple fixed-effects Poisson model produces consistent and reasonably efficient estimates under more general conditions than commonly assumed. We also show through replication of existing papers that economic conclusions can be highly sensitive to the regression model employed.

Keywords: Empirical methods; Count data; Poisson regression (search for similar items in EconPapers)
JEL-codes: C18 C23 G00 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (70)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:146:y:2022:i:2:p:529-551

DOI: 10.1016/j.jfineco.2022.08.004

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