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Methoden der ifo Kurzfristprognose am Beispiel der Ausrüstungsinvestitionen

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Author Info

  • Anna Billharz
  • Steffen Elstner
  • Marcus Jüppner

Abstract

Konjunkturprognosen sind für die Planung von Unternehmen und des Staats eine wichtige Bezugs- und Argumentationsgrundlage. Der Artikel zeigt anhand des Beispiels der Ausrüstungsinvestitionen die Herangehensweise bei der Prognose eines Verwendungsaggregats des deutschen BIP. Die Prognose umfasst dabei das laufende sowie das kommende Jahr und wird in zwei Schritten erstellt. Zuerst erfolgt die Kurzfristprognose, bei der das aktuelle sowie das folgende Quartal prognostiziert werden. Sie zeichnet sich dadurch aus, dass für diesen Zeitraum schon Informationen in Form von Indikatoren vorliegen. Der restliche Zeitraum wird anhand ökonometrischer Strukturmodelle prognostiziert, wobei dynamische Eigenschaften der betreffenden Zeitreihe und der verschiedenen Bezugszeitreihen fortgeschrieben werden.

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Bibliographic Info

Article provided by Ifo Institute for Economic Research at the University of Munich in its journal ifo Schnelldienst.

Volume (Year): 65 (2012)
Issue (Month): 21 (November)
Pages: 24-33

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Handle: RePEc:ces:ifosdt:v:65:y:2012:i:21:p:24-33

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Related research

Keywords: Konjunkturprognose; Sozialprodukt; Anlageinvestition; Konjunkturindikator; Prognose; Deutschland; Ausrüstungsinvestition; Bruttoinlandsprodukt;

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References

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  1. Carstensen, Kai & Wohlrabe, Klaus & Ziegler, Christina, 2011. "Predictive ability of business cycle indicators under test: A case study for the Euro area industrial production," Munich Reprints in Economics 19953, University of Munich, Department of Economics.
  2. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
  3. Oliver Hülsewig & Johannes Mayr & Dirk Ulbricht, 2007. "Zur Evaluierung von VAR-Prognosen," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 60(07), pages 19-25, 04.
  4. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
  5. Steffen Elstner & Christian Grimme & Thomas Siemsen, 2010. "Die größten aufstrebenden Märkte für deutsche Exporte liegen in Asien und Osteuropa," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 63(16), pages 22-25, 08.
  6. Steffen Henzel & Johannes Mayr, 2009. "The Virtues of VAR Forecast Pooling – A DSGE Model Based Monte Carlo Study," Ifo Working Paper Series Ifo Working Paper No. 65, Ifo Institute for Economic Research at the University of Munich.
  7. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
  8. Daniel Detzer & Christian R. Proaño & Katja Rietzler & Sven Schreiber & Thomas Theobald & Sabine Stephan, 2012. "Verfahren der konjunkturellen Wendepunktbestimmung unter Berücksichtigung der Echtzeit-Problematik," IMK Studies 27-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
  9. Wolfgang Nierhaus & Jan-Egbert Sturm, 2003. "Methoden der Konjunkturprognose," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 56(04), pages 7-23, 02.
  10. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
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Cited by:
  1. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.

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