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Forecasting Euro Area Real GDP: Optimal Pooling of Information

Author

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  • Oliver Hülsewig
  • Johannes Mayr
  • Timo Wollmershäuser

Abstract

This paper proposes a new method of forecasting euro area quarterly real GDP that uses area-wide indicators, which are derived by optimally pooling the information contained in national indicator series. Following the ideas of predictive modeling, we construct the area-wide indicators by utilizing weights that minimize the variance of the out-of-sample forecast errors of the area-wide target variable. In an out-of-sample forecast experiment we find that our optimal pooling of information approach outperforms alternative forecasting methods in terms of forecast accuracy.

Suggested Citation

  • Oliver Hülsewig & Johannes Mayr & Timo Wollmershäuser, 2008. "Forecasting Euro Area Real GDP: Optimal Pooling of Information," CESifo Working Paper Series 2371, CESifo.
  • Handle: RePEc:ces:ceswps:_2371
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    References listed on IDEAS

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    1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    2. Giannone, Domenico & De Mol, Christine & Daubechies, Ingrid & Brodie, Joshua, 2007. "Sparse and Stable Markowitz Portfolios," CEPR Discussion Papers 6474, C.E.P.R. Discussion Papers.
    3. George Kapetanios & Massimiliano Marcellino, 2009. "A parametric estimation method for dynamic factor models of large dimensions," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 208-238, March.
    4. Roy Batchelor, 2001. "How useful are the forecasts of intergovernmental agencies? The IMF and OECD versus the consensus," Applied Economics, Taylor & Francis Journals, vol. 33(2), pages 225-235.
    5. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    7. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    8. Anna Stangl, 2007. "World Economic Survey," Chapters, in: Georg Goldrian (ed.), Handbook of Survey-Based Business Cycle Analysis, chapter 5, Edward Elgar Publishing.
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    Citations

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    Cited by:

    1. Evgenia Kudymowa & Johanna Garnitz & Klaus Wohlrabe, 2013. "Ifo World Economic Survey and Real Economic Developments in Selected Countries," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(19), pages 23-30, October.
    2. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(54), pages 5802-5816.
    3. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    4. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    5. Evgenia Kudymowa & Johanna Garnitz & Klaus Wohlrabe, 2014. "Ifo World Economic Survey and the Business Cycle in Selected Countries," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(04), pages 51-57, January.
    6. Evgenia Kudymowa & Johanna Garnitz & Klaus Wohlrabe, 2014. "Ifo World Economic Survey and the Business Cycle in Selected Countries," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(4), pages 51-57, January.
    7. 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, October.

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    More about this item

    Keywords

    forecasting; aggregation; model averaging; real time experiment;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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