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Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production

Author

Listed:
  • Carstensen Kai

    (Ifo Institute for Economic Research and University of Munich, Poschingerstraße 5, 81679 Munich, Germany)

  • Wohlrabe Klaus

    (Ifo Institute for Economic Research, Poschingerstraße 5, 81679 Munich, Germany)

  • Ziegler Christina

    (University of Leipzig, Grimmaische Straße 12, 04109 Leipzig, Germany)

Abstract

In this paper we assess the information content of seven widely cited early indicators for the euro area with respect to forecasting area-wide industrial production. To this end, we use various tests that are designed to compare competing forecast models. In addition to the standard Diebold-Mariano test, we employ tests that account for specific problems typically encountered in forecast exercises. Specifically, we pay attention to nested model structures, we alleviate the problem of data snooping arising from multiple pairwise testing, and we analyze the structural stability in the relative forecast performance of one indicator compared to a benchmark model. Moreover, we consider loss functions that overweight forecast errors in booms and recessions to check-whether a specific indicator that appears to be a good choice on average is also preferable in times of economic stress. We find that none of this indicators uniformly dominates all its competitors. The optimal choice rather depends on the specific forecast situation and the loss function of the user. For 1-month forecasts the business climate indicator of the European Commission and the OECD composite leading indicator generally work well, for 6-month forecasts the OECD composite leading indicator performs very good by all criteria, and for 12-month forecasts the FAZ-Euro indicator published by the Frankfurter Allgemeine Zeitung is the only one that can beat the benchmark AR(1) model.

Suggested Citation

  • Carstensen Kai & Wohlrabe Klaus & Ziegler Christina, 2011. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 82-106, February.
    • Handle: RePEc:jns:jbstat:v:231:y:2011:i:1:p:82-106
    DOI: 10.1515/jbnst-2011-0107
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    Cited by:

    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    2. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    3. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    4. Cyrille Lenoel & Garry Young, 2020. "Real-time turning point indicators: Review of current international practices," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-05, Economic Statistics Centre of Excellence (ESCoE).
    5. repec:ipg:wpaper:2014-471 is not listed on IDEAS
    6. Christoph Schinke, 2016. "Wealth and Politics: Studies on Inter Vivos Transfers and Partisan Effects," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 67.
    7. Jan-Christoph Rülke, 2011. "Do private sector forecasters desire to deviate from the German council of economic experts?," WHU Working Paper Series - Economics Group 11-04, WHU - Otto Beisheim School of Management.
    8. Pablo Duarte & Bernd Süssmuth, 2018. "Implementing an Approximate Dynamic Factor Model to Nowcast GDP Using Sensitivity Analysis," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 127-141, April.
    9. Elsayyad, May & Konrad, Kai A., 2012. "Fighting multiple tax havens," Journal of International Economics, Elsevier, vol. 86(2), pages 295-305.
    10. Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016. "Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation," Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
    11. Ha Quyen Ngo & Niklas Potrafke & Marina Riem & Christoph Schinke, 2018. "Ideology and Dissent among Economists: The Joint Economic Forecast of German Economic Research Institutes," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 44(1), pages 135-152, January.
    12. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    13. Anna Billharz & Steffen Elstner & Marcus Jüppner, 2012. "Methoden der ifo Kurzfristprognose am Beispiel der Ausrüstungsinvestitionen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(21), pages 24-33, November.
    14. Brückbauer, Frank & Schröder, Michael, 2021. "Data resource profile: The ZEW FMS dataset," ZEW Discussion Papers 21-100, ZEW - Leibniz Centre for European Economic Research.
    15. Yashkir, Olga & Yashkir, Yuriy, 2013. "Monitoring of Credit Risk through the Cycle: Risk Indicators," MPRA Paper 46402, University Library of Munich, Germany.
    16. 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.
    17. Rülke Jan-Christoph, 2012. "Do Private Sector Forecasters Desire to Deviate From the German Council of Economic Experts?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 414-428, August.
    18. Marina Riem, 2017. "Essays on the Behavior of Firms and Politicians," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 73.
    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. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.

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    More about this item

    Keywords

    Weighted loss; leading indicators; euro area; forecasting; Weighted loss; leading indicators; euro area; forecasting;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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