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Makroökonomische Prognosen mit gemischten Frequenzen

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  • Klaus Wohlrabe

    ()

Abstract

Die Prognose von makroökonomischen Zeitreihen steht häufig vor dem Problem, dass die verwendeten Indikatoren und die Zielzeitreihe in verschiedenen Frequenzen vorliegen. So ist das Bruttoinlandsprodukt nur auf Quartalsbasis verfügbar, während die meisten Indikatoren, wie z.B. das ifo Geschäftsklima, monatlich erhoben werden. Der vorliegende Artikel stellt zwei Modellklassen aus der neueren Literatur vor, die Schätzungen mit gemischten Frequenzen ohne aggregationsbedingten Informationsverlust erlauben: MIDAS-Modelle und VAR-Zustandsraummodelle. Diese Verfahren haben den Vorteil, dass hochfrequente Informationen, die innerhalb einer Periode hinzukommen, in die Prognose einbezogen werden können. Anhand einer Fallstudie für das deutsche BIP wird gezeigt, dass die neuen Modellklassen genauere Prognosen als die üblichen Zeitreihenmodelle liefern. Die neuen Verfahren werden am ifo Institut zur Kurzfristprognose eingesetzt.

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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): 62 (2009)
Issue (Month): 21 (November)
Pages: 22-33

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Handle: RePEc:ces:ifosdt:v:62:y:2009:i:21:p:22-33

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Keywords: Prognose; Zeitreihenanalyse; Prognoseverfahren; VAR-Modell; Deutschland; MIDAS-Modell;

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References

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  1. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, August.
  2. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
  3. 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.
  4. 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.
  5. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
  6. Bharat Trehan, 1992. "Predicting contemporaneous output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
  7. Angelini, Elena & Henry, Jérôme & Marcellino, Massimiliano, 2004. "Interpolation and Backdating with A Large Information Set," CEPR Discussion Papers 4533, C.E.P.R. Discussion Papers.
  8. Terry J. Fitzgerald & Preston J. Miller, 1989. "A simple way to estimate current-quarter GNP," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 27-31.
  9. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  10. Oliver Hülsewig & Johannes Mayr & Stéphane Sorbe, 2007. "Assessing the Forecast Properties of the CESifo World Economic Climate Indicator: Evidence for the Euro Area," Ifo Working Paper Series Ifo Working Paper No. 46, Ifo Institute for Economic Research at the University of Munich.
  11. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  12. 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.
  13. Shen, Chung-Hua, 1996. "Forecasting macroeconomic variables using data of different periodicities," International Journal of Forecasting, Elsevier, vol. 12(2), pages 269-282, June.
  14. 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.
  15. Wohlrabe, Klaus, 2009. "Forecasting with mixed-frequency time series models," Munich Dissertations in Economics 9681, University of Munich, Department of Economics.
  16. 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.
  17. Evans, Martin D.D., 2005. "Where Are We Now? Real-Time Estimates of the Macro Economy," CEPR Discussion Papers 5270, C.E.P.R. Discussion Papers.
  18. Jens Hogrefe, 2008. "Forecasting data revisions of GDP: a mixed frequency approach," AStA Advances in Statistical Analysis, Springer, vol. 92(3), pages 271-296, August.
  19. Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
  20. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  21. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, September.
  22. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(1), pages 31-67.
  23. Giuseppe Parigi & Roberto Golinelli, 2007. "The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 77-94.
  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. Bharat Trehan, 1989. "Forecasting growth in current quarter real GNP," Economic Review, Federal Reserve Bank of San Francisco, issue Win, pages 39-52.
  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. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
  28. Christian Seiler, 2009. "Prediction Qualities of the Ifo Indicators on a Temporal Disaggregated German GDP," Ifo Working Paper Series Ifo Working Paper No. 67, Ifo Institute for Economic Research at the University of Munich.
  29. Kitchen, John & Monaco, Ralph, 2003. "Real-Time Forecasting in Practice: The U.S. Treasury Staff's Real-Time GDP Forecast System," MPRA Paper 21068, University Library of Munich, Germany, revised Oct 2003.
  30. Namwon Hyung & Clive W.J. Granger, 2008. "Linking series generated at different frequencies This work is part of a PhD dissertation presented at the University of California, San Diego (1999)," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 95-108.
  31. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  32. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
  33. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
  34. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
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Cited by:
  1. Anna Billharz & Steffen Elstner & Marcus Jüppner, 2012. "Methoden der ifo Kurzfristprognose am Beispiel der Ausrüstungsinvestitionen," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 65(21), pages 24-33, November.
  2. Klaus Wohlrabe, 2012. "Prognose des Dienstleistungssektors in Deutschland," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 65(01), pages 31-39, 01.
  3. Klaus Wohlrabe, 2011. "Konstruktion von Indikatoren zur Analyse der wirtschaftlichen Aktivität in den Dienstleistungsbereichen," ifo Forschungsberichte, Ifo Institute for Economic Research at the University of Munich, number 55, October.
  4. 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.
  5. 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.

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