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Testing Predicitive Ability of Business Cycle Indicators for the Euro Area

Author

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  • Christina Ziegler

Abstract

We analyze the predictive power of seven leading indicators for economic activity in the Euro Area developed by different banks, institutions and research centers. Our comparison is conducted in a bivariate vector autoregressive framework. Indicators are compared by means of an in-sample and an out-of-sample forecasting experiment. Predictive accuracy is compared by recently proposed tests for superior predictive ability. Our results suggest that nearly all indicators have good in-sample properties and that a majority of them is able to outperform a naive univariate autoregressive model out-of-sample. Additionally, we find that indicators perform better in boom periods than in recessions. The OECD and FAZ indicators are both composite indicators and deliver the most accurate forecasts.

Suggested Citation

  • Christina Ziegler, 2009. "Testing Predicitive Ability of Business Cycle Indicators for the Euro Area," ifo Working Paper Series 69, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  • Handle: RePEc:ces:ifowps:_69
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    File URL: http://www.cesifo-group.de/DocDL/IfoWorkingPaper-69.pdf
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    References listed on IDEAS

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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
    2. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    3. Schumacher Christian & Dreger Christian, 2004. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models? / Die Schätzung von großen Faktormodellen für die deutsche Volkswirtschaft: Übertreffen sie ei," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 731-750, December.
    4. Fritsche Ulrich & Stephan Sabine, 2002. "Leading Indicators of German Business Cycles. An Assessment of Properties / Frühindikatoren der deutschen Konjunktur. Eine Beurteilung ihrer Eigenschaften," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 222(3), pages 289-315, June.
    5. Jonas Dovern & Christina Ziegler, 2008. "Predicting Growth Rates and Recessions. Assessing U.S. Leading Indicators under Real-Time Condition," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 54(4), pages 293-318.
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    9. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
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    Cited by:

    1. Yashkir, Olga & Yashkir, Yuriy, 2013. "Monitoring of Credit Risk through the Cycle: Risk Indicators," MPRA Paper 46402, University Library of Munich, Germany.

    More about this item

    Keywords

    Leading indicators; Euro area; forecasting;

    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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