A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct

Abstract

This article describes the use of flexible Bayesian regression models for estimating a partially identified probability function. Our approach permits efficient sensitivity analysis concerning the posterior impact of priors on the partially identified component of the regression model. The new methodology is illustrated on an important problem where only partially observed data are available—inferring the prevalence of accounting misconduct among publicly traded U.S. businesses. Supplementary materials for this article are available online.

• Bayesian inference
• Nonlinear regression
• Partial identification
• Sampling bias
• Sensitivity analysis
• Set identification.