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Vorlaufeigenschaften von Ifo-Indikatoren für Westdeutschland

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  • Ulrich Fritsche

Abstract

Ifo business climate and other Ifo indicators will be investigated to assess its properties. Properties of Ifo indicators either following the old institutional classification or the newer possibility of use classification will be checked against long-term time series according the new statistical classification (NACE or WZ 93). The business cycle component is detrended using an Hodrick Prescott filter. The series are tested for structural stability. The results show structural breaks, but, after the break, the stability of leading properties became stronger. Long-term indicators have no worse stability properties than short-term indicators - that means their use for forecasting is possible. Ifo-Indikatoren werden auf ihre Vorlaufeigenschaften, auf Granger-Kausalität, die Stabilität der Vorlaufbeziehung und einen Strukturbruch untersucht. Da die Ifo-Reihen noch nicht auf die neue Gliederung der amtlichen Statistik (WZ 93) umgestellt wurden, wird erstmals die Eignung der verschiedenen Indikatoren nach institutioneller und Verwendungszweckgliederung gemessen an den umbasierten Produktionsindexreihen nach WZ 93 beurteilt. Ebenfalls neu ist, daß zur Beurteilung der Konjunkturkomponente eine normierte Trendabweichung von einem Hodrick-Prescott-Filter benutzt wurde. Die These der Strukturkonstanz ist zurückzuweisen; die Stabilität der Vorlaufbeziehung in den 90er Jahren ist enger geworden. Die Indikatoren mit längerem Vorlauf (Geschäftserwartungen und Produktionspläne) weisen keine nachweisbar geringere Stabilität auf.

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  • Ulrich Fritsche, 1999. "Vorlaufeigenschaften von Ifo-Indikatoren für Westdeutschland," Discussion Papers of DIW Berlin 179, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp179
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    References listed on IDEAS

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    Cited by:

    1. Hinze, Jörg, 2003. "Prognoseleistung von Frühindikatoren: Die Bedeutung von Frühindikatoren für Konjunkturprognosen - Eine Analyse für Deutschland," HWWA Discussion Papers 236, Hamburg Institute of International Economics (HWWA).
    2. Klaus Abberger & Sascha Becker & Barbara Hofmann & Klaus Wohlrabe, 2007. "Mikrodaten im ifo Institut für Wirtschaftsforschung – Bestand, Verwendung und Zugang," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 1(1), pages 27-42, June.
    3. Werner Hölzl & Gerhard Schwarz, 2014. "Der WIFO-Konjunkturtest: Methodik und Prognoseeigenschaften," WIFO Monatsberichte (monthly reports), WIFO, vol. 87(12), pages 835-850, December.
    4. 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.
    5. Hüfner, Felix P. & Lahl, David, 2003. "What Determines the ZEW Indicator?," ZEW Discussion Papers 03-48, ZEW - Leibniz Centre for European Economic Research.
    6. 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.
    7. Benner Joachim & Meier Carsten-Patrick, 2004. "Prognosegüte alternativer Früh Indikatoren für die Konjunktur in Deutschland / Forecasting Performance of Alternative Indicators for the German Economy," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 639-652, December.
    8. Benner, Joachim & Meier, Carsten-Patrick, 2005. "Was leisten Stimmungsindikatoren für die Prognose des realen Bruttoinlandsprodukts in Deutschland? Eine Echtzeit-Analyse," Open Access Publications from Kiel Institute for the World Economy 3725, Kiel Institute for the World Economy (IfW Kiel).
    9. Sascha O. Becker & Klaus Wohlrabe, 2008. "European Data Watch: Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(2), pages 307-319.
    10. Hüfner, Felix P. & Schröder, Michael, 2001. "Unternehmens- versus Analystenbefragungen: Zum Prognosegehalt von ifo-Geschäftserwartungen und ZEW-Konjunkturerwartungen," ZEW Discussion Papers 01-04, ZEW - Leibniz Centre for European Economic Research.
    11. Christian Seiler, 2009. "Prediction Qualities of the Ifo Indicators on a Temporal Disaggregated German GDP," ifo Working Paper Series 67, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    12. Richard Etter & Michael Graff, 2003. "Estimating and Forecasting Production and Orders in Manufacturing Industry from Business Survey Data: Evidence from Switzerland, 1990-2003," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 139(IV), pages 507-533, December.
    13. Hinze, Jorg, 2003. "Prognoseleistung von Fruhindikatoren: Die Bedeutung von Fruhindikatoren fur Konjunk-turprognosen - Eine Analyse fur Deutschland," Discussion Paper Series 26253, Hamburg Institute of International Economics.
    14. Benner, Joachim & Meier, Carsten-Patrick, 2003. "Prognosegüte alternativer Frühindikatoren für die Konjunktur in Deutschland," Kiel Working Papers 1139, Kiel Institute for the World Economy (IfW Kiel).
    15. Schröder, Michael & Hüfner, Felix P., 2002. "Forecasting economic activity in Germany: how useful are sentiment indicators?," ZEW Discussion Papers 02-56, ZEW - Leibniz Centre for European Economic Research.

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    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • L70 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - General

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