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

P. Richard Hahn, Jared S. Murray and Ioanna Manolopoulou

Journal of the American Statistical Association, 2016, vol. 111, issue 513, 14-26

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.

Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2015.1084307 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:111:y:2016:i:513:p:14-26

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2015.1084307

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().