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Prognosegüte alternativer Frühindikatoren für die Konjunktur in Deutschland

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Author Info

  • Joachim Benner

    ()
    (Universität Kiel)

  • Carsten-Patrick Meier

    ()
    (Universität Kiel)

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    Abstract

    Untersuchungen zur Prognosegüte sollten nicht nur Prognosefehler, die auf der Schätzung der Parameter beruhen berücksichtigen, sondern auch solche, die aus der stichprobenabhängigen Auswahl des Prognosemodells resultieren. Wird die Prognosefehlervarianz durch rekursive Out-of-Sample Prognosen geschätzt, so sollte dabei nicht nur die Parameterschätzung, sondern auch die Modellselektion rekursiv vorgenommen werden. Wir wenden dieses Prinzip auf die Analyse der Prognosegüte dreier wichtiger Indikatoren für die Konjunktur in Deutschland an, den vom ifo-Institut erhobenen "Geschäftserwartungen", den vom Zentrum für Europäische Wirtschaftsforschung veröffentlichten "Konjunkturerwartungen" und des von der "Wirtschaftswoche" berechneten "Early bird "- Indikators. Es zeigt sich, dass die Prognosefehler bei der realistischeren rekursiven Modellauswahl größer sind als bei nicht-rekursiver Spezifikation. Die untersuchten Indikatoren liefern unter bestimmten Umständen bessere Prognosen als ein einfaches autoregressives Modell.

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    Bibliographic Info

    Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

    Volume (Year): 224 (2004)
    Issue (Month): 6 (November)
    Pages: 639-652

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    Handle: RePEc:jns:jbstat:v:224:y:2004:i:6:p:639-652

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    Related research

    Keywords: Modellselektion; Frühindikatoren; Konjunkturprognose;

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    References

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    1. Norbert Funke, 1997. "Predicting recessions: Some evidence for Germany," Review of World Economics (Weltwirtschaftliches Archiv), Springer, vol. 133(1), pages 90-102, March.
    2. repec:wop:humbsf:2000-37 is not listed on IDEAS
    3. Ulrich Fritsche, 2001. "Do probit models help in forecasting turning points of German business cycles?," Macroeconomics 0012022, EconWPA.
    4. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Macroeconomics 0004005, EconWPA.
    5. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    6. Kilian, Lutz, 2001. "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(3), pages 161-79, April.
    7. Franses, Philip Hans & Lucas, Andre, 1998. "Outlier Detection in Cointegration Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 459-68, October.
    8. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
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    10. Ulrich Fritsche, 1999. "Vorlaufeigenschaften von Ifo-Indikatoren für Westdeutschland," Discussion Papers of DIW Berlin 179, DIW Berlin, German Institute for Economic Research.
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    Cited by:
    1. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," Ifo Working Paper Series Ifo Working Paper No. 57, Ifo Institute for Economic Research at the University of Munich.
    2. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.

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