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The performance of short-term forecasts of the German economy before and during the 2008/2009 recession

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  • Drechsel, Katja
  • Scheufele, Rolf

Abstract

The paper analyzes the forecasting performance of leading indicators for industrial production in Germany. We focus on single and pooled leading indicator models both before and during the financial crisis. Pairwise and joint significant tests are used to evaluate single indicator models, as well as forecast combination methods. In addition, we investigate the stability of forecasting models during the most recent financial crisis. We find that only a small number of single indicator models were performing well before the crisis. Pooling can substantially increase the reliability of leading indicator forecasts. During the crisis, the relative performances of many leading indicator models (e.g. using surveys, term and risk spreads) improved.

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  • 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.
  • Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:428-445
    DOI: 10.1016/j.ijforecast.2011.04.003
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    Cited by:

    1. Steffen Henzel & Sebastian Rast, 2013. "Prognoseeigenschaften von Indikatoren zur Vorhersage des Bruttoinlandsprodukts in Deutschland," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(17), pages 39-46, September.
    2. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    3. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    4. 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.
    5. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW).
    6. Christian Seiler, 2014. "Mode Preferences in Business Surveys: Evidence from Germany," ifo Working Paper Series 193, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    7. Jens Boysen-Hogrefe, 2012. "A note on predicting recessions in the euro area using real M1," Economics Bulletin, AccessEcon, vol. 32(2), pages 1291-1301.
    8. Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    9. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
    10. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    11. Christian Seiler, 2014. "The determinants of unit non-response in the Ifo Business Survey," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 161-177, September.
    12. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    13. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    14. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    15. Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," CESifo Working Paper Series 6457, CESifo Group Munich.
    16. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    17. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2017. "Forecasting GDP all over the World: Evidence from Comprehensive Survey Data," MPRA Paper 81772, University Library of Munich, Germany.
    18. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.
    19. Katja Rietzler & Sabine Stephan, 2012. "Monthly recession predictions in real time: A density forecast approach for German industrial production," IMK Working Paper 94-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    20. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    21. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland, Institute for Economies in Transition.

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