A Bayesian partial identification approach to inferring the prevalence of accounting misconduct
Methodology
2015-03-10 v3
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.
Keywords
Cite
@article{arxiv.1407.8430,
title = {A Bayesian partial identification approach to inferring the prevalence of accounting misconduct},
author = {P. Richard Hahn and Jared S. Murray and Ioanna Manolopoulou},
journal= {arXiv preprint arXiv:1407.8430},
year = {2015}
}