Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70518

Modeling Skewness Determinants in Accounting Research

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Item Summary

Title:Modeling Skewness Determinants in Accounting Research
Authors:Sudipta Basu
Dmitri Byzalov
Keywords:Pearson's Skewness
Quantile-Based Skewness
Rolling Window
Conditional Distribution
Gmm
Date Issued:15 Aug 2020
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.
URI:http://hdl.handle.net/10125/70518
Appears in Collections: 16 Financial Accounting 9: Fair Value Accounting/ Intangible Assets/Innovations


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