Accounting for uncertainty: an application of Bayesian methods to accruals models
为实证会计研究提供贝叶斯估计方法的应用介绍，通过对比标准方法，展示贝叶斯方法如何将参数和模型不确定性纳入正常应计估计，从而提高盈余管理检验的效力并减少误判。
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.