Modeling Skewness Determinants in Accounting Research

Date
2020-08-15
Authors
Basu, Sudipta
Byzalov, Dmitri
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Abstract
Skewness-based proxies are widely used in accounting and finance research. To study how the skewness of a dependent variable Y varies with conditioning variables X, researchers typically compute firm-specific skewness measures over a short window and regress them on X. However, we show that this standard approach can cause severe biases and produce false findings of both conditional skewness on average and systematic variation in conditional skewness. We develop alternative methods that address these biases by directly modeling the conditional skewness of Y for each observation as a function of X. Simulations confirm that our methods have good type-I errors and test power even in scenarios in which the standard method is severely biased. Our methods are transparent, robust, and can be implemented in a few lines of code. Use of our methods changes major prior findings in the literature.
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Keywords
Pearson's Skewness, Quantile-Based Skewness, Rolling Window, Conditional Distribution, Gmm
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