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

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  • P. Richard Hahn
  • Jared S. Murray
  • Ioanna Manolopoulou

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.

Suggested Citation

  • P. Richard Hahn & Jared S. Murray & Ioanna Manolopoulou, 2016. "A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 14-26, March.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:513:p:14-26
    DOI: 10.1080/01621459.2015.1084307
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    Cited by:

    1. Christian A. Gregory, 2020. "Are We Underestimating Food Insecurity? Partial Identification with a Bayesian 4-Parameter IRT Model," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 632-655, October.
    2. Martijn van Hasselt & Christopher R. Bollinger & Jeremy W. Bray, 2022. "A Bayesian approach to account for misclassification in prevalence and trend estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 351-367, March.
    3. Ashton, John & Burnett, Tim & Diaz-Rainey, Ivan & Ormosi, Peter, 2021. "Known unknowns: How much financial misconduct is detected and deterred?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    4. Michelle Xia, 2018. "Bayesian Adjustment for Insurance Misrepresentation in Heavy-Tailed Loss Regression," Risks, MDPI, vol. 6(3), pages 1-16, August.
    5. Dyck, Alexander & Morse, Adair & Zingales, Luigi, 2023. "How pervasive is corporate fraud?," Working Papers 327, The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State.
    6. Dan Amiram & Zahn Bozanic & James D. Cox & Quentin Dupont & Jonathan M. Karpoff & Richard Sloan, 2018. "Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature," Review of Accounting Studies, Springer, vol. 23(2), pages 732-783, June.
    7. Francis DiTraglia & Camilo García-Jimeno, 2016. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," NBER Working Papers 22621, National Bureau of Economic Research, Inc.
    8. Francis J. DiTraglia & Camilo Garcia-Jimeno, 2020. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," Papers 2011.07276, arXiv.org.

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