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

Paul Hahn, Jared S. Murray, Ioanna Manolopoulou

Research output: Contribution to journalArticle

8 Citations (Scopus)

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

Fingerprint

Partial Identification
Regression Model
Probability function
Bayesian Model
Sensitivity Analysis
Methodology
Regression model
Partial identification

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct. / Hahn, Paul; Murray, Jared S.; Manolopoulou, Ioanna.

In: Journal of the American Statistical Association, Vol. 111, No. 513, 02.01.2016, p. 14-26.

Research output: Contribution to journalArticle

@article{7f3db2660d53462b9abfd3cdac3ec7ad,
title = "A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct",
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.",
keywords = "Bayesian inference, Nonlinear regression, Partial identification, Sampling bias, Sensitivity analysis, Set identification",
author = "Paul Hahn and Murray, {Jared S.} and Ioanna Manolopoulou",
year = "2016",
month = "1",
day = "2",
doi = "10.1080/01621459.2015.1084307",
language = "English (US)",
volume = "111",
pages = "14--26",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "513",

}

TY - JOUR

T1 - A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct

AU - Hahn, Paul

AU - Murray, Jared S.

AU - Manolopoulou, Ioanna

PY - 2016/1/2

Y1 - 2016/1/2

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - Nonlinear regression

KW - Partial identification

KW - Sampling bias

KW - Sensitivity analysis

KW - Set identification

UR - http://www.scopus.com/inward/record.url?scp=84969822622&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84969822622&partnerID=8YFLogxK

U2 - 10.1080/01621459.2015.1084307

DO - 10.1080/01621459.2015.1084307

M3 - Article

AN - SCOPUS:84969822622

VL - 111

SP - 14

EP - 26

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 513

ER -