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IFOCAST: Methoden der ifo-Kurzfristprognose

Author

Listed:
  • Kai Carstensen
  • Steffen Henzel
  • Johannes Mayr
  • Klaus Wohlrabe

Abstract

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.

Suggested Citation

  • Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
  • Handle: RePEc:ces:ifosdt:v:62:y:2009:i:23:p:15-28
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    References listed on IDEAS

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    1. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    2. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 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.
    3. 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.
    4. Fritsche Ulrich & Stephan Sabine, 2002. "Leading Indicators of German Business Cycles. An Assessment of Properties / Frühindikatoren der deutschen Konjunktur. Eine Beurteilung ihrer Eigenschaften," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 222(3), pages 289-315, June.
    5. Franck Sédillot & Nigel Pain, 2003. "Indicator Models of Real GDP Growth in Selected OECD Countries," OECD Economics Department Working Papers 364, OECD Publishing.
    6. Jan-Egbert Sturm & Timo Wollmershäuser (ed.), 2005. "Ifo Survey Data in Business Cycle and Monetary Policy Analysis," Contributions to Economics, Springer, number 978-3-7908-1605-1.
    7. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    9. Jan Jacobs & Jan-Egbert Sturm, 2005. "Do Ifo Indicators Help Explain Revisions in German Industrial Production?," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 93-114, Springer.
    10. Projektgruppe Gemeinschaftsdiagnose, 2009. "Gemeinschaftsdiagnose Frühjahr 2009: Im Sog der Weltrezession," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(08), pages 03-81, April.
    11. 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.
    12. 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.
    13. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    14. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    15. Stefan Mittnik & Peter Zadrozny, 2005. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 19-48, Springer.
    16. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    17. 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.
    18. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, June.
    19. Marie Diron, 2008. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 371-390.
    20. K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze & G. Rünstler, 2008. "Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise," Working Paper Research 133, National Bank of Belgium.
    21. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    22. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    23. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    24. Hahn, Elke & Skudelny, Frauke, 2008. "Early estimates of euro area real GDP growth: a bottom up approach from the production side," Working Paper Series 975, European Central Bank.
    25. Wohlrabe, Klaus, 2009. "Forecasting with mixed-frequency time series models," Munich Dissertations in Economics 9681, University of Munich, Department of Economics.
    26. Peter Grasmann & Filip Keereman, 2001. "An indicator-based short-term forecast for quarterly GDP in the euro area," European Economy - Economic Papers 2008 - 2015 154, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    27. K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze & G. Rünstler, 2008. "Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise," Working Paper Research 133, National Bank of Belgium.
    28. 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(1), pages 108-124, April.
    29. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    30. Klaus Abberger & Wolfgang Nierhaus, 2008. "Die ifo Kapazitätsauslastung - ein gleichlaufender Indikator der deutschen Industriekonjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(16), pages 15-23, August.
    31. Hüfner Felix P. & Schröder Michael, 2002. "Prognosegehalt von ifo-Geschäftserwartungen und ZEW-Konjunkturerwartungen: Ein ökonometrischer Vergleich / Forecasting German industrial Production: An Econometric Comparison of ifo- and ZEW-Business ," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 222(3), pages 316-336, June.
    32. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    33. 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.
    34. Steffen Henzel & Johannes Mayr, 2009. "The Virtues of VAR Forecast Pooling – A DSGE Model Based Monte Carlo Study," ifo Working Paper Series 65, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
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    More about this item

    Keywords

    Konjunkturprognose; Prognoseverfahren; Deutschland;
    All these keywords.

    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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