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The impact of seasonal and price adjustments on the predictability of German GDP revisions

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  • Boysen-Hogrefe, Jens
  • Neuwirth, Stefan

Abstract

Releases of the GDP are subject to revisions over time. This paper examines the predictability of German GDP revisions using forecast rationality tests. Previous studies of German GDP covering data until 1997 finds that revisions of real seasonally adjusted GDP are predictable. This paper uses a newly available real-time data to analyze the revisions of real seasonal adjusted GDP, of nominal unadjusted GDP, of the seasonal pattern, and of the GDP deflator for the period between 1992 and 2006. We find that the revisions of the nominal unadjusted GDP are unpredictable, but that the revisions of the price adjustments are predictable. Nevertheless, revisions of real seasonally adjusted GDP are hardly predictable and less well predictable compared to earlier studies. This lower predictability seems to be linked to the finding that revisions of seasonal adjustments are hardly predictable, too, and that their predictability decreased over time.

Suggested Citation

  • Boysen-Hogrefe, Jens & Neuwirth, Stefan, 2012. "The impact of seasonal and price adjustments on the predictability of German GDP revisions," Kiel Working Papers 1753, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:1753
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    Cited by:

    1. Pascal Bührig & Klaus Wohlrabe, 2015. "Revisions of German Industrial Production Statistics and Ifo Indicators," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(21), pages 27-31, November.
    2. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    3. Pascal Bührig & Klaus Wohlrabe, 2016. "Forecasting revisions of German industrial production," Applied Economics Letters, Taylor & Francis Journals, vol. 23(15), pages 1062-1064, October.
    4. Andreas Dibiasi & Samad Sarferaz, 2020. "Measuring Macroeconomic Uncertainty: The Labor Channel of Uncertainty from a Cross-Country Perspective," Papers 2006.09007, arXiv.org, revised Dec 2020.
    5. Döhrn, Roland, 2018. "Revisionen der Volkswirtschaftlichen Gesamtrechnungen: Revisionspraxis des Statistischen Bundesamtes und ihre Auswirkungen auf Prognosen," RWI Materialien 127, RWI - Leibniz-Institut für Wirtschaftsforschung.
    6. Roland Döhrn, 2019. "Revisionen der Volkswirtschaftlichen Gesamtrechnungen und ihre Auswirkungen auf Prognosen [Revisions of national accounts data and their impact on forecasts]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 13(2), pages 99-123, September.
    7. Timo Wollmershäuser, 2016. "Forecasting Revisions of Stock Changes Using Ifo Inventory Assessments," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(07), pages 26-32, April.
    8. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    9. Dibiasi, Andreas & Sarferaz, Samad, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, vol. 153(C).

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    More about this item

    Keywords

    real-time data; GDP revisions; noise; news; forecasting; seasonal adjustment; price adjustment;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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