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Out-of-sample Performance of Leading Indicators for the German Business Cycle: Single vs. Combined Forecasts

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  • Christian Dreger

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  • Christian Schumacher

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Abstract

In this paper the forecasting performance of popular leading indicators for the German business cycle is investigated. Survey based indicators (ifo business climate, ZEW index of economic sentiment) and composite leading indicators (Handelsblatt, Frankfurter Allgemeine Zeitung, Commerzbank) are considered. The analysis points to a significant relationship of the indicators to the business cycle within the sample period, as measured by the direction of causality. But, their out-of-sample forecasts do not improve the autoregressive benchmark. This result may be caused by structural breaks in the out-of-sample period. As combinations of forecasts tend to be more robust against such shifts, pooled forecasts are constructed using different methods of aggregation, including linear combinations of forecasts and common factor models. In contrast to the single indicator approach, the combined indicator forecasts are able to beat the benchmark at each forecasting horizon. Therefore, the analysis points to the usefulness of pooling information in order to get more reliable forecasts.

Suggested Citation

  • 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, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 71-87.
  • Handle: RePEc:oec:stdkaa:5km7v183qs0v
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    File URL: http://dx.doi.org/10.1787/jbcma-2005-5km7v183qs0v
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    Cited by:

    1. Österholm, Pär, 2013. "Forecasting Business Investment in the Short Term Using Survey Data," Working Papers 131, National Institute of Economic Research.
    2. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    3. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    4. Klaus Abberger & Gebhard Flaig & Wolfgang Nierhaus, 2007. "ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 33.
    5. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    6. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 36.
    7. Katarina Bacic & Maruska Vizek, 2006. "A Brand New CROLEI: Do We Need a New Forecasting Index?," Financial Theory and Practice, Institute of Public Finance, vol. 30(4), pages 311-346.
    8. Kholodilin Konstantin A., 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(6), pages 653-674, December.
    9. Maria Antoinette Silgoner, 2005. "An Overview of European Economic Indicators: Great Variety of Data on the Euro Area, Need for More Extensive Coverage of the New EU Member States," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 66-89.
    10. Raoufina, Karine, 2016. "Forecasting Employment Growth in Sweden Using a Bayesian VAR Model," Working Papers 144, National Institute of Economic Research.
    11. Sascha O. Becker & Klaus Wohlrabe, 2008. "European Data Watch: Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(2), pages 307-319.
    12. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    13. repec:jns:jbstat:v:227:y:2007:i:1:p:87-101 is not listed on IDEAS
    14. Anna Wolf, 2007. "Der Index für Konjunkturerwartungen des ZEW und des ifo Instituts, WES, sind für Deutschland identisch," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 60(03), pages 55-56, February.
    15. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
    16. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    17. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    18. Christian Dreger & Konstantin Arkadievich Kholodilin, 2013. "Forecasting Private Consumption by Consumer Surveys," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 10-18, January.
    19. 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.
    20. Klaus Abberger & Sascha Becker & Barbara Hofmann & Klaus Wohlrabe, 2007. "Mikrodaten im ifo Institut für Wirtschaftsforschung – Bestand, Verwendung und Zugang," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 1(1), pages 27-42, June.
    21. Heinisch, Katja & Scheufele, Rolf, 2017. "Should forecasters use real-time data to evaluate leading indicator models for GDP prediction? German evidence," IWH Discussion Papers 5/2017, Halle Institute for Economic Research (IWH).
    22. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
    23. Vogt Gerit, 2007. "Analyse der Prognoseeigenschaften von ifo-Konjunkturindikatoren unter Echtzeitbedingungen / The Forecasting Performance of ifo-indicators Under Real-time Conditions," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 227(1), pages 87-101, February.

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    Keywords

    Leading Indicators; Combined forecasts;

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