Modeling data revisions: Measurement error and dynamics of "true" values

Jan P. A. M. Jacobs, Simon van Norden*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

30 Citations (Scopus)

Abstract

Policy makers must base their decisions on preliminary and partially revised data of varying reliability. Realistic modeling of data revisions is required to guide decision makers in their assessment of current and future conditions. This paper provides a new framework with which to model data revisions.

Recent empirical work suggests that measurement errors typically have much more complex dynamics than existing models of data revisions allow. This paper describes a state-space model that allows for richer dynamics in these measurement errors, including the noise, news and spillover effects documented in this literature. We also show how to relax the common assumption that "true" values are observed after a few revisions.

The result is a unified and flexible framework that allows for more realistic data revision properties, and allows the use of standard methods for optimal real-time estimation of trends and cycles. We illustrate the application of this framework with real-time data on US real output growth. (C) 2011 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)101-109
Number of pages9
JournalJournal of Econometrics
Volume161
Issue number2
DOIs
Publication statusPublished - Apr-2011

Keywords

  • Real-time analysis
  • Data revisions
  • REAL-TIME DATA
  • STATE-SPACE APPROACH
  • PROVISIONAL DATA
  • UNITED-KINGDOM
  • BUSINESS-CYCLE
  • FINAL VINTAGE
  • REVISED DATA
  • OUTPUT-GAP
  • DATA SET
  • GDP

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