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Predicting Germany's recessions with leading indicators: Evidence from probit models

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  • Döpke, Jörg

Abstract

Probit models are employed to evaluate leading indicators for Germany's recessions. The predictive power of leading indicators is found to be lower than assumed in previous studies. Although, monetary variables provide the best predictive power for recessions, survey data and order inflows show a lag rather than a lead to the recession time series. US interest rates have also some information content with respect to the German cycle. Constructing a model with a set of variables to predict recessions does not help to improve the forecasts. The out-of-sample performance of the indicators appeals to be even worse.

Suggested Citation

  • Döpke, Jörg, 1999. "Predicting Germany's recessions with leading indicators: Evidence from probit models," Kiel Working Papers 944, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:944
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    Cited by:

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    2. Ulrich Fritsche, 2001. "Do probit models help in forecasting turning points of German business cycles?," Macroeconomics 0012022, University Library of Munich, Germany.
    3. 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.
    4. Klaus Abberger & Gebhard Flaig & Wolfgang Nierhaus, 2007. "ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 33.
    5. Gottschalk, Jan, 2002. "Keynesian and monetarist views on the German unemployment problem: theory and evidence," Kiel Working Papers 1096, Kiel Institute for the World Economy (IfW Kiel).
    6. Ulrich Fritsche & Vladimir Kuzin, 2002. "Do Leading Indicators Help to Predict Business Cycle Turning Points in Germany?," Discussion Papers of DIW Berlin 314, DIW Berlin, German Institute for Economic Research.
    7. 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).
    8. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    9. 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.
    10. Ulrich FRITSCHE & Vladimir KOUZINE, 2010. "Prediction of Business Cycle Turning Points in Germany," EcoMod2004 330600054, EcoMod.
    11. 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.

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    More about this item

    Keywords

    Leading indicators; business cycles; probit models;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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