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
Date Written: February 8, 2016
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
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