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Activity Number: 66
Type: Topic Contributed
Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
Sponsor: Survey Research Methods Section
Abstract - #308285
Title: Revisions Revisited: Data-Driven Approaches for Detection in Quarterly Financial Report Macro-Level Data
Author(s): Gregory Cepluch*+ and Melissa McDaniel and Laura Bechtel
Companies: U.S. Census Bureau and U.S. Census Bureau and U.S. Census Bureau
Keywords: Revisions ; Quarterly Financial Report ; Process Control
Abstract:

The Quarterly Financial Report (QFR) program investigates statistical methods for identifying substantial macro-level revisions of the income statement and balance sheet data. Currently, macro-level relative revisions are identified as suspect if the absolute values are above a defined threshold, which is determined by subject matter expertise. In this paper, process control methodologies are explored to detect substantial revisions in the data, specifically p-charts and stair-step charts. The inputs necessary for these control charts are not readily available for revision estimates. As a result, a focus is placed upon estimating the control chart parameters. Once these parameters are developed, various evaluation diagnostics are employed to assess their validity. Finally, the performances of the new and existing revision identification methodologies are compared.


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