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

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  • Carstensen, Kai
  • Wohlrabe, Klaus
  • Ziegler, Christina

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 on average three indicators have superior forecast ability, namely the EuroCoin indicator, the OECD composite leading indicator, and the FAZ-Euro indicator published by the Frankfurter Allgemeine Zeitung. If one is interested in one-month forecasts only, the business climate indicator of the European Commission yields the smallest errors. However, the results are not completely invariant against the choice of the loss function. Moreover, rolling local tests reveal that the indicators are particularly useful in times of unusual changes in industrial production while the simple autoregressive benchmark is difficult to beat during time of average production growth.

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Bibliographic Info

Paper provided by University of Munich, Department of Economics in its series Discussion Papers in Economics with number 11442.

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Date of creation: Mar 2010
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Handle: RePEc:lmu:muenec:11442

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Keywords: weighted loss; leading indicators; euro area; forecasting;

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References

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  1. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
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  13. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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Cited by:
  1. Elsayyad, May & Konrad, Kai A., 2012. "Fighting multiple tax havens," Journal of International Economics, Elsevier, vol. 86(2), pages 295-305.
  2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
  3. 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.
  4. Yashkir, Olga & Yashkir, Yuriy, 2013. "Monitoring of Credit Risk through the Cycle: Risk Indicators," MPRA Paper 46402, University Library of Munich, Germany.

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