Accounting for Uncertainty: An Application of Bayesian Methods to Accruals Models

73 Pages Posted: 16 Jul 2019 Last revised: 18 Nov 2020

See all articles by Matthias Breuer

Matthias Breuer

Columbia University

Harm H. Schütt

Tilburg University - Tilburg School of Economics and Management

Date Written: July 9, 2019

Abstract

We provide an applied introduction to Bayesian estimation methods for empirical accounting research. To showcase the methods, we compare and contrast the estimation of accruals models via a Bayesian approach with the literature’s standard approach. The standard approach takes a given model of normal accruals for granted and neglects any uncertainty about the model and its parameters. By contrast, our Bayesian approach allows incorporating parameter and model uncertainty into the estimation of normal accruals. This approach can increase power and reduce false positives in tests for opportunistic earnings management as a result of better estimates of normal accruals and more robust inferences. We advocate the greater use of Bayesian methods in accounting research, especially since they can now be easily implemented in popular statistical software packages.

Keywords: Accruals, Earnings Management, Prediction, Bayes

JEL Classification: C11, C53, M40

Suggested Citation

Breuer, Matthias and Schütt, Harm H., Accounting for Uncertainty: An Application of Bayesian Methods to Accruals Models (July 9, 2019). Columbia Business School Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=3417406 or http://dx.doi.org/10.2139/ssrn.3417406

Matthias Breuer (Contact Author)

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Harm H. Schütt

Tilburg University - Tilburg School of Economics and Management ( email )

PO Box 90153
Tilburg, 5000 LE Ti
Netherlands

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