A Bayesian partial identification approach to inferring the prevalence of accounting misconduct

TL;DR

This paper introduces a Bayesian regression method for estimating the prevalence of accounting misconduct using partially observed data, enabling sensitivity analysis on prior assumptions.

Contribution

It presents a novel Bayesian approach for partial identification in prevalence estimation, specifically applied to accounting misconduct detection.

Findings

Effective sensitivity analysis of priors on misconduct prevalence

Application to publicly traded U.S. companies

Demonstrates utility with partially observed data

Abstract

This paper 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 is available - inferring the prevalence of accounting misconduct among publicly traded U.S. businesses.