Forecasting with real-time macroeconomic data: The ragged-edge problem and revisions

27695 Bouwman, J.P.A.M. Jacobs

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)


Real-time macroeconomic data are typically incomplete for today and the immediate past ('ragged edge') and subject to revision. To enable more timely forecasts the recent missing data have to be imputed. The paper presents a state-space model that can deal with publication lags and data revisions. The framework is applied to the US leading index. We conclude that including even a simple model of data revisions improves the accuracy of the imputations and that the univariate imputation method in levels adopted by The Conference Board can be improved upon. (C) 2011 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)784-792
Number of pages9
JournalJournal of Macroeconomics
Issue number4
Publication statusPublished - Dec-2011


  • Data revisions
  • Publication lags
  • Data imputations
  • Leading index
  • State space models
  • Kalman filter
  • GDP

Cite this