A two-step quantile regression method for discretionary accounting

May Huaxi Zhang (), Stanley Iat-Meng Ko, Andreas Karathanasopoulos () and Chia Chun Lo ()
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May Huaxi Zhang: Beijing Institute of Technology
Andreas Karathanasopoulos: University of Dubai
Chia Chun Lo: Georgia State University

Review of Quantitative Finance and Accounting, 2022, vol. 59, issue 1, No 1, 22 pages

Abstract: Abstract This paper proposes an analytical approach that complements the traditional two-step linear regression and one-single step linear regression suggested by Chen et al. (J Account Res 56:751–796, 2018). Using the regression residual as the dependent variable in a second regression is a procedure commonly used in studying discretionary accounting. Chen et al. (J Account Res 56:751–796, 2018) propose to adopt one-step regression to avoid estimation bias and inference error. However, the mean level effect estimated by one-step OLS regression is not sufficient to capture the overall spectrum of discretionary accounting behaviors and thus may mislead its user in drawing implications. We use two-stage quantile regression to examine determinants of discretionary accounting such as discretionary accruals, discretionary expense, discretionary book-tax differences, and abnormal investment in different quantiles. We illustrate the differences between the one-step regression and our two-step quantile regression using four common discretionary accounting studies. Our results and implications reconcile, to some extent, the contradictory findings between results of the one-step OLS regression and the previous established works based on two-step regression.

Keywords: Two-stage; Residuals; Coefficient bias; Quantile regression; Discretionary accruals (search for similar items in EconPapers)
JEL-codes: C18 G10 G30 M40 M41 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11156-022-01048-w

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