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

Hahn, P. Richard; Murray, Jared; Manolopoulou, Ioanna

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

Hahn, P. Richard, Jared S. Murray, and Ioanna Manolopoulou. "A Bayesian partial identification approach to inferring the prevalence of accounting misconduct." Journal of the American Statistical Association (2016): 1-37, Forthcoming

40 Pages Posted: 10 Feb 2016

See all articles by P. Richard Hahn

P. Richard Hahn

Arizona State University (ASU) - School of Mathematical and Statistical Sciences

Jared Murray

University of Texas at Austin - Red McCombs School of Business

Ioanna Manolopoulou

University College London - Department of Statistical Science

Date Written: February 8, 2016

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 are available – inferring the prevalence of accounting misconduct among publicly traded U.S. businesses.

Keywords: Bayesian inference, nonlinear regression, partial identification, sampling bias, sensitivity analysis, set identification

Suggested Citation: Suggested Citation

Hahn, P. Richard and Murray, Jared and Manolopoulou, Ioanna, A Bayesian Partial Identification Approach to Inferring the Prevalance of Accounting Misconduct (February 8, 2016). Hahn, P. Richard, Jared S. Murray, and Ioanna Manolopoulou. "A Bayesian partial identification approach to inferring the prevalence of accounting misconduct." Journal of the American Statistical Association (2016): 1-37, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2729636