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Prognosegüte alternativer Frühindikatoren für die Konjunktur in Deutschland

  • Joachim Benner
  • Carsten-Patrick Meier
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    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 verö.entlichten „Konjunkturerwartungen" und des von der "Wirtschaftswoche" berechneten „Earlybird"-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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    Paper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1139.

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    Length: 12 pages
    Date of creation: Mar 2003
    Date of revision:
    Handle: RePEc:kie:kieliw:1139
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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
    2. Bruce E. Hansen, 1999. "Discussion of 'Data mining reconsidered'," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 192-201.
    3. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Macroeconomics 0004005, EconWPA.
    4. Ulrich Fritsche, 1999. "Vorlaufeigenschaften von Ifo-Indikatoren für Westdeutschland," Discussion Papers of DIW Berlin 179, DIW Berlin, German Institute for Economic Research.
    5. Ulrich Fritsche, 2001. "Do probit models help in forecasting turning points of German business cycles?," Macroeconomics 0012022, EconWPA.
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