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IFOCAST: Methoden der ifo-Kurzfristprognose

  • Kai Carstensen
  • Steffen Henzel

    ()

  • Johannes Mayr
  • Klaus Wohlrabe

    ()

Die Einschätzung und Vorhersage der gesamtwirtschaftlichen Situation im laufenden und im folgenden Quartal ist eine der zentralen Aufgaben der Konjunkturprognose. Das ifo Institut stützt sich bei seiner Kurzfristprognose des Bruttoinlandsprodukts auf den dreistufigen IFOCAST-Ansatz. In der ersten Stufe werden monatlich verfügbare Indikatoren, wie z.B. das ifo Geschäftsklima, extrapoliert und auf Quartalsebene aggregiert. Besonderes Augenmerk gilt dabei der Industrieproduktion, die mit Hilfe disaggregierter ifo-Umfragedaten fortgeschrieben wird. In einem zweiten Schritt wird die Bruttowertschöpfung der einzelnen Wirtschaftsbereiche mit Hilfe von Brückengleichungen prognostiziert. Im Rahmen eines Kombinationsansatzes wird eine Vielzahl von Modellen kombiniert, um dem Aspekt der Modellunsicherheit Rechnung zu tragen. In einem dritten Schritt werden die Quartalsprognosen einzelner Wirtschaftsbereiche anhand der ökonomischen Gewichte zur Prognose des Bruttoinlandsprodukts aggregiert. Es hat sich sowohl in der Prognoseliteratur als auch in der praktischen Umsetzung gezeigt, dass der gewählte Ansatz eine zuverlässige Kurzfristprognose liefert und flexibel genug ist, um auch extreme Entwicklungen gut aufzuzeigen. Zusätzlich zu diesem mehrstufigen Standardverfahren werden in diesem Artikel Mixed-Frequency-Modelle und Boosting-Algorithmen vorgestellt, welche den Standardansatz im Probebetrieb ergänzen.

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

Volume (Year): 62 (2009)
Issue (Month): 23 (December)
Pages: 15-28

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Handle: RePEc:ces:ifosdt:v:62:y:2009:i:23:p:15-28
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  1. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
  2. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
  3. Elena Angelini & Gonzalo Camba-Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2008. "Short-Term Forecasts of Euro Area GDP Growth," Working Papers ECARES ECARES 2008-035, ULB -- Universite Libre de Bruxelles.
  4. Ulrich Fritsche & Sabine Stephan, 2002. "Leading Indicators of German Business Cycles - An Assessment of Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 222(3), pages 289-315.
  5. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
  6. 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.
  7. Stefan Mittnik & Peter A. Zadrozny, 2004. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data," CESifo Working Paper Series 1203, CESifo Group Munich.
  8. Jan J.J. Groen & George Kapetanios, 2008. "Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting," Working Papers 624, Queen Mary University of London, School of Economics and Finance.
  9. Wohlrabe, Klaus, 2009. "Forecasting with mixed-frequency time series models," Munich Dissertations in Economics 9681, University of Munich, Department of Economics.
  10. Anindya Banerjee & Massimiliano Marcellino, 2003. "Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth?," Working Papers 236, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  11. Buhlmann P. & Yu B., 2003. "Boosting With the L2 Loss: Regression and Classification," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 324-339, January.
  12. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
  13. Karim Barhoumi & Szilard Benk & Riccardo Cristadoro & Ard Den Reijer & Audrone Jakaitiene & Piotr Jelonek & António Rua & Gerhard Rünstler & Karsten Ruth & Christophe Van Nieuwenhuyze, 2008. "Short-term forecasting of GDP using large monthly datasets - a pseudo real-time forecast evaluation exercise," Occasional Paper Series 84, European Central Bank.
  14. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  15. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
  16. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  17. Klaus Abberger & Wolfgang Nierhaus, 2008. "Die ifo Kapazitätsauslastung - ein gleichlaufender Indikator der deutschen Industriekonjunktur," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 61(16), pages 15-23, 08.
  18. David Hendry & Michael Clements, 2001. "Pooling of Forecasts," Economics Series Working Papers 2002-W09, University of Oxford, Department of Economics.
  19. Franck Sédillot & Nigel Pain, 2003. "Indicator Models of Real GDP Growth in Selected OECD Countries," OECD Economics Department Working Papers 364, OECD Publishing.
  20. Hahn, Elke & Skudelny, Frauke, 2008. "Early estimates of euro area real GDP growth: a bottom up approach from the production side," Working Paper Series 0975, European Central Bank.
  21. Felix Hüfner & Michael Schröder, 2002. "Prognosegehalt von ifo-Geschäftserwartungen und ZEW-Konjunkturerwartungen: Ein ökonometrischer Vergleich," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 222(3), pages 316-336.
  22. 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.
  23. Jan Jacobs & Jan-Egbert Sturm, 2004. "Do Ifo Indicators Help Explain Revisions in German Industrial Production?," CESifo Working Paper Series 1205, CESifo Group Munich.
  24. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: An application to German GDP," CEPR Discussion Papers 7197, C.E.P.R. Discussion Papers.
  25. 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.
  26. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
  27. Peter Grasmann & Filip Keereman, 2001. "An indicator-based short-term forecast for quarterly GDP in the euro area," European Economy - Economic Papers 154, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
  28. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
  29. Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, vol. 4(01), pages 108-124, April.
  30. Diron, Marie, 2006. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Working Paper Series 0622, European Central Bank.
  31. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
  32. Projektgruppe Gemeinschaftsdiagnose, 2009. "Gemeinschaftsdiagnose Frühjahr 2009: Im Sog der Weltrezession," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 62(08), pages 03-81, 04.
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