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Count (and count-like) data in finance

Author

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

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.

Suggested Citation

  • Cohn, Jonathan B. & Liu, Zack & Wardlaw, Malcolm I., 2022. "Count (and count-like) data in finance," Journal of Financial Economics, Elsevier, vol. 146(2), pages 529-551.
    • Handle: RePEc:eee:jfinec:v:146:y:2022:i:2:p:529-551
    DOI: 10.1016/j.jfineco.2022.08.004
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    References listed on IDEAS

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    More about this item

    Keywords

    Empirical methods; Count data; Poisson regression;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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