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

Paul Hahn; Jared S. Murray; Ioanna Manolopoulou

doi:10.1080/01621459.2015.1084307

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

Paul Hahn, Jared S. Murray, Ioanna Manolopoulou

Research output: Contribution to journal › Article

10 Scopus citations

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.

Original language	English (US)
Pages (from-to)	14-26
Number of pages	13
Journal	Journal of the American Statistical Association
Volume	111
Issue number	513
DOIs	https://doi.org/10.1080/01621459.2015.1084307
State	Published - Jan 2 2016
Externally published	Yes

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Keywords

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

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

Cite this

Hahn, P., Murray, J. S., & Manolopoulou, I. (2016). A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct. Journal of the American Statistical Association, 111(513), 14-26. https://doi.org/10.1080/01621459.2015.1084307

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10.1080/01621459.2015.1084307