Count Data in Finance

Abstract

This paper examines the use of count data-based outcome variables such as corporate patents in empirical corporate finance research. We demonstrate that the common practice of regressing the log of one plus the count on covariates ("LOG1PLUS" regression) produces biased and inconsistent estimates of objects of interest and lacks meaningful interpretation. Poisson regressions have simple interpretations and produce unbiased and consistent estimates under standard exogeneity assumptions, though they lose efficiency if the count data is overdispersed. Replicating several recent papers on corporate patenting, we find that LOG1PLUS and Poisson regressions frequently produce meaningfully different estimates and that bias in LOG1PLUS regressions is likely large.