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

• 成果类型: Article
• 署名作者: Hahn, P. Richard; Murray, Jared S.; Manolopoulou, Ioanna
• 署名单位: University of Chicago; Carnegie Mellon University; University of London; University College London
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1084307
• 发表日期: 2016
• 页码: 14-26
• 关键词: sensitivity-analysis; Identifiability; models; priors

摘要

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 availableinferring the prevalence of accounting misconduct among publicly traded U.S. businesses. Supplementary materials for this article are available online.