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

Paul Hahn, Jared S. Murray, Ioanna Manolopoulou

Research output: Contribution to journalArticle

11 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 languageEnglish (US)
Pages (from-to)14-26
Number of pages13
JournalJournal of the American Statistical Association
Volume111
Issue number513
DOIs
StatePublished - Jan 2 2016
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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