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Testing Predicitive Ability of Business Cycle Indicators for the Euro Area

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  • Christina Ziegler

Abstract

We analyze the predictive power of seven leading indicators for economic activity inthe Euro Area developed by different banks, institutions and research centers. Ourcomparison is conducted in a bivariate vector autoregressive framework. Indicators arecompared 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 amajority 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 inrecessions. The OECD and FAZ indicators are both composite indicators and deliver themost accurate forecasts.

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Bibliographic Info

Paper provided by Ifo Institute for Economic Research at the University of Munich in its series Ifo Working Paper Series with number Ifo Working Paper No. 69.

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Date of creation: 2009
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Handle: RePEc:ces:ifowps:_69

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Related research

Keywords: Leading indicators; Euro area; forecasting;

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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. Konstantin A. Kholodilin & Boriss Siliverstovs, 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), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 226(3), pages 234-259, May.
  3. Christian Schumacher & Christian Dreger, 2004. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 224(6), pages 731-750, November.
  4. Mario Forni & Filippo Altissimo & Riccardo Cristadoro & Marco Lippi & Giovanni Veronese., 2008. "New Eurocoin: Tracking Economic Growth in Real Time," Center for Economic Research (RECent) 020, University of Modena and Reggio E., Dept. of Economics.
  5. Jonas Dovern & Christina Ziegler, 2008. "Predicting Growth Rates and Recessions. Assessing U.S. Leading Indicators Under Real-Time Conditions," Kiel Working Papers 1397, Kiel Institute for the World Economy.
  6. Ulrich Fritsche & Sabine Stephan, 2002. "Leading Indicators of German Business Cycles - An Assessment of Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 222(3), pages 289-315.
  7. Kwiatkowski, D. & Phillips, P.C.B. & Schmidt, P., 1990. "Testing the Null Hypothesis of Stationarity Against the Alternative of Unit Root : How Sure are we that Economic Time Series have a Unit Root?," Papers 8905, Michigan State - Econometrics and Economic Theory.
  8. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  9. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
  10. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  11. 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.
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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.

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