The Importance of Separating the Probability of Committing and Detecting Misstatements in the Restatement Setting

• 成果类型: Article
• 署名作者: Barton, F. Jane; Burnett, Brian M.; Gunny, Katherine; Miller, Brian P.
• 署名单位: City University of New York (CUNY) System; Baruch College (CUNY); University of North Carolina; University of North Carolina Charlotte; University of Colorado System; University of Colorado Denver; Indiana University System; Indiana University Bloomington; IU Kelley School of Business
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4627
• 发表日期: 2024
• 关键词: misstatements; RESTATEMENTS; occurrence and detection; bivariate probit

摘要

This study demonstrates the importance of separating the probabilities of misstatement occurrence and detection when examining financial statement restatements. Despite the many benefits of examining the probability of restatements using traditional logistic models, interpretations of these models are clouded by partial observability-only subsequently detected misstatements are observable. We propose addressing this often overlooked issue by implementing a bivariate probit model with partial observability. We demonstrate the importance of separating these latent probabilities by re-examining three prior restatement studies and show the importance of separating the occurrence and detection probabilities. Our evidence suggests that future studies interested in restatements as a measure of accounting quality should consider implementing bivariate probit models as one way to address the partial observability inherent in this setting.