Count (and count-like) data in finance

Abstract

count (and count-like) data in finance ∗ (accepted, journal of financial economics) jonathan cohn university of texas-austin zack liu university of houston malcolm wardlaw university of georgia july 2022 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. ∗jonathan cohn: jonathan.cohn@mccombs.utexas.edu, (512) 232-6827. zack liu: zliu@bauer.uh.edu, (713) 743-4764. malcolm wardlaw: malcolm.wardlaw@uga.edu, (706) 204-9295. toni whited was the editor for this article. we would like to thank jason abrevaya, kenneth ahern, pat akey, andres almazan, aydoğan altı, tony cookson, sergio correia, john griffin, daniel henderson, travis johnson, praveen kumar, sam krueger, aaron pancost, paul povel, james scott, sheridan titman, toni whited, jeff wooldridge, and participants in the virtual finance seminar, the financial research association …

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