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Prediction of Business Cycle Turning Points in Germany / Prognose konjunktureller Wendepunkte in Deutschland

Author

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  • Fritsche Ulrich

    (DIW Berlin (German Institute for Economic Research), Königin-Luise-Str. 5, D-14195 Berlin. Tel.: ++49/+30/89789315, Germany)

  • Kuzin Vladimir

    (Goethe-Universität Frankfurt am Main, Institut für Statistik und Methoden der Ökonometrie, Gräfstr. 78, D-60054 Frankfurt a.M. Tel.: ++49/+69/798 25332, Fax: ++49/+69/79823662, Germany)

Abstract

Using a binary reference series based on the dating procedure of Artis, Kontolemis and Osborn (1997) different procedures for predicting turning points of the German business cycles were tested. Specifically, a probit model as proposed by Estrella and Mishkin (1997) as well as Markov-switching models were taken into consideration. The overall results indicate that the interest rate spread, the real effective exchange rate as well as some monetary indicators and some survey indicators can help to predict turning points of the German business cycle. The models were estimated for the in-sample period 1978 to 1997 and the reliability of the results was tested out of that sample (1998 to 2002).

Suggested Citation

  • Fritsche Ulrich & Kuzin Vladimir, 2005. "Prediction of Business Cycle Turning Points in Germany / Prognose konjunktureller Wendepunkte in Deutschland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(1), pages 22-43, February.
    • Handle: RePEc:jns:jbstat:v:225:y:2005:i:1:p:22-43
    DOI: 10.1515/jbnst-2005-0103
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    Cited by:

    1. Jan Grossarth-Maticek & Johannes Mayr, 2008. "Medienberichte als Konjunkturindikator," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(07), pages 17-29, April.
    2. Kholodilin Konstantin A., 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(6), pages 653-674, December.
    3. 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.
    4. Konstantin A. Kholodilin, 2005. "Forecasting the Turns of German Business Cycle: Dynamic Bi-factor Model with Markov Switching," Discussion Papers of DIW Berlin 494, DIW Berlin, German Institute for Economic Research.
    5. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    6. Beate Schirwitz, 2009. "A comprehensive German business cycle chronology," Empirical Economics, Springer, vol. 37(2), pages 287-301, October.
    7. Rülke Jan-Christoph, 2012. "Do Private Sector Forecasters Desire to Deviate From the German Council of Economic Experts?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 414-428, August.
    8. 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.
    9. Drechsel, Katja & Scheufele, Rolf, 2011. "The Financial Crisis from a Forecaster’s Perspective," IWH Discussion Papers 5/2011, Halle Institute for Economic Research (IWH).
    10. Beate Schirwitz & Christian Seiler & Klaus Wohlrabe, 2009. "Regionale Konjunkturzyklen in Deutschland – Teil II: Die Zyklendatierung," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(14), pages 24-31, July.

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