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Flow of conjunctural information and forecast of euro area economic activity

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  • Katja Drechsel
  • Laurent Maurin

Abstract

Combining forecasts, we analyse the role of information flow in computing short-term forecasts up to one quarter ahead for the euro area GDP and its main components. A dataset of 114 monthly indicators is set up and simple bridge equations are estimated. The individual forecasts are then pooled, using different weighting schemes. To take into consideration the release calendar of each indicator, six forecasts are compiled successively during the quarter. We found that the sequencing of information determines the weight allocated to each block of indicators, especially when the first month of hard data becomes available. This conclusion extends the findings of the recent literature. Moreover, when combining forecasts, two weighting schemes are found to outperform the equal weighting scheme in almost all cases. Compared to an AR forecast, these improve by more than 40% the forecast performance for GDP in the current and next quarter. Copyright (C) 2010 John Wiley & Sons, Ltd.

Suggested Citation

  • Katja Drechsel & Laurent Maurin, 2011. "Flow of conjunctural information and forecast of euro area economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 336-354, April.
  • Handle: RePEc:jof:jforec:v:30:y:2011:i:3:p:336-354
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    File URL: http://hdl.handle.net/10.1002/for.1177
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    Citations

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    Cited by:

    1. Katja Drechsel & Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    2. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    3. Laura D’Amato & Lorena Garegnani & Emilio Blanco, 2011. "Using the Flow of High Frequency Information for Short Term Forecasting of Economic Activity in Argentina," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(64), pages 7-33, October -.
    4. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW).
    5. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    6. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    7. Katja Drechsel & Rolf Scheufele, 2012. "The Financial Crisis from a Forecaster’s Perspective," Credit and Capital Markets, Credit and Capital Markets, vol. 45(1), pages 1-26.
    8. Marie Bessec, 2010. "Étalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Économie et Prévision, Programme National Persée, vol. 193(2), pages 77-99.
    9. Laura D'Amato & Lorena Garegnani & Emilio Blanco, 2016. "GDP Nowcasting: Assessing the Cyclical Conditions of the Argentine Economy," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(74), pages 7-26, December.
    10. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    11. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(02), pages 363-393, March.
    12. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    13. Robert Lehmann & Klaus Wohlrabe, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, Verein für Socialpolitik, vol. 16(2), pages 226-254, May.
    14. Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100393, Verein für Socialpolitik / German Economic Association.
    15. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    16. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    17. Laura D´Amato & Lorena Garegnani & Emilio Blanco, 2015. "GDP Nowcasting: Assessing business cycle conditions in Argentina," BCRA Working Paper Series 201569, Central Bank of Argentina, Economic Research Department.

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