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Analyse der Prognoseeigenschaften von ifo-Konjunkturindikatoren unter Echtzeitbedingungen

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  • Gerit Vogt

    ()
    (ifo Institut für Wirtschaftsforschung)

Abstract

In recent years some paers have been bublished that deal with the forecasting performance of indicators for the German economy. The real-time aspect, however, was largely neglected. This article analyses the information content of some ifo indicators (the business climate index for the manufacturing sector and its components, the current business situation and business expectations) to predict the German index of production. The analysis is based on cross correlations, Granger causality tests and different out-of-sample forecasts, generated by ubset VAR models. First, the out-of-sample forecasts are made, as in conventional studies, with the latest available data and fixed model structure. Afterwards, the out-of-sample indicator properties are analysed in real-time, i.e. with real-time data and variable model structure. In general the indicator properties become worse under real-time conditions. The indicator-based VAR models are not able to beat the forecast performance of a pur autoregressive model for forecast horizons of one and three month. But for forecast horizons of six, nine and twelve months, the indicators seem to be useful in predicting the index of production.

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Bibliographic Info

Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

Volume (Year): 227 (2007)
Issue (Month): 1 (February)
Pages: 87-101

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Handle: RePEc:jns:jbstat:v:227:y:2007:i:1:p:87-101

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Related research

Keywords: Business cycles; ifo-indicators; real-time data;

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References

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  1. Athanasios Orphanides & Simon van Norden, 2003. "The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time," CIRANO Working Papers 2003s-01, CIRANO.
  2. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  3. Athanasios Orphanides & Simon Van_Norden, 2000. "The Reliability of Output Gap Estimates in Real Time," Econometric Society World Congress 2000 Contributed Papers 0768, Econometric Society.
  4. Döpke, Jörg, 2004. "Real-time data and business cycle analysis in Germany," Discussion Paper Series 1: Economic Studies 2004,11, Deutsche Bundesbank, Research Centre.
  5. Tom Stark and Dean Croushore, 2001. "Forecasting with a Real-Time Data Set for Macroeconomists," Computing in Economics and Finance 2001 258, Society for Computational Economics.
  6. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Discussion Papers of DIW Berlin 207, DIW Berlin, German Institute for Economic Research.
  7. Christian Dreger & Christian Schumacher, 2005. "Out-of-sample Performance of Leading Indicators for the German Business Cycle: Single vs. Combined Forecasts," Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2005(1), pages 71-87.
  8. Konstantin A. Kholodilin & Boriss Siliverstovs, 2005. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Discussion Papers of DIW Berlin 522, DIW Berlin, German Institute for Economic Research.
  9. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  10. Jörg Döpke, 2004. "Real-Time Data and Business Cycle Analysis in Germany," Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2004(3), pages 337-361.
  11. Brüggemann, Ralf & Lütkepohl, Helmut, 2000. "Lag selection in subset VAR models with an application to a US monetary system," SFB 373 Discussion Papers 2000,37, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  12. John C. Robertson & Ellis W. Tallman, 1998. "Data vintages and measuring forecast model performance," Economic Review, Federal Reserve Bank of Atlanta, issue Q 4, pages 4-20.
  13. Jan Jacobs & Jan-Egbert Sturm, 2004. "Do Ifo Indicators Help Explain Revisions in German Industrial Production?," CESifo Working Paper Series 1205, CESifo Group Munich.
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Citations

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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.
  2. Anna Scharschmidt & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
  3. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, Ifo Institute for Economic Research at the University of Munich, number 36.

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