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Forecasting real-time data allowing for data revisions

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Author Info

  • Kosei Fukuda

    (College of Economics, Nihon University, Tokyo, Japan)

Abstract

A modeling approach to real-time forecasting that allows for data revisions is shown. In this approach, an observed time series is decomposed into stochastic trend, data revision, and observation noise in real time. It is assumed that the stochastic trend is defined such that its first difference is specified as an AR model, and that the data revision, obtained only for the latest part of the time series, is also specified as an AR model. The proposed method is applicable to the data set with one vintage. Empirical applications to real-time forecasting of quarterly time series of US real GDP and its eight components are shown to illustrate the usefulness of the proposed approach.  Copyright © 2007 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1032
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 26 (2007)
Issue (Month): 6 ()
Pages: 429-444

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Handle: RePEc:jof:jforec:v:26:y:2007:i:6:p:429-444

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Jonas Dovern & Christina Ziegler, 2008. "Predicting Growth Rates and Recessions. Assessing U.S. Leading Indicators under Real-Time Condition," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 54(4), pages 293-318.

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