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Forecasting real-time data allowing for data revisions

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  • 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.

Suggested Citation

  • Kosei Fukuda, 2007. "Forecasting real-time data allowing for data revisions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(6), pages 429-444.
    • Handle: RePEc:jof:jforec:v:26:y:2007:i:6:p:429-444
    DOI: 10.1002/for.1032
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    References listed on IDEAS

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    Cited by:

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    2. 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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