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Combining the forecasts in the ECB survey of professional forecasters: can anything beat the simple average?

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  • Genre, Véronique
  • Kenny, Geoff
  • Meyler, Aidan
  • Timmermann, Allan

Abstract

In this paper, we explore the potential gains from alternative combinations of the surveyed forecasts in the ECB Survey of Professional Forecasters. Our analysis encompasses a variety of methods including statistical combinations based on principal components analysis and trimmed means, performance-based weighting, least squares estimates of optimal weights as well as Bayesian shrinkage. We provide a pseudo real-time out-of-sample performance evaluation of these alternative combinations and check the sensitivity of the results to possible data-snooping bias. The latter robustness check is also informed using a novel real time meta selection procedure which is not subject to the data-snooping critique. For GDP growth and the unemployment rate, only few of the forecast combination schemes are able to outperform the simple equal-weighted average forecast. Conversely, for the inflation rate there is stronger evidence that more refined combinations can lead to improvement over this benchmark. In particular, for this variable, the relative improvement appears significant even controlling for data snooping bias. JEL Classification: C22, C53

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 1277.

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Date of creation: Dec 2010
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Handle: RePEc:ecb:ecbwps:20101277

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Related research

Keywords: data snooping; forecast combination; forecast evaluation; real-time data; Survey of Professional Forecasters;

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References

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  1. Domenico Giannone & Michèle Lenza & Daphné Momferatu & Luca Onorante, 2010. "Short-term inflation projections: a Bayesian vector autoregressive approach," Working Papers ECARES ECARES 2010-011, ULB -- Universite Libre de Bruxelles.
  2. Giannone, Domenico & Henry, Jérôme & Lalik, Magdalena & Modugno, Michele, 2010. "An area-wide real-time database for the euro area," Working Paper Series 1145, European Central Bank.
  3. Francis X. Diebold & Peter Pauly, 1987. "The use of prior information in forecast combination," Special Studies Papers 218, Board of Governors of the Federal Reserve System (U.S.).
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Cited by:
  1. Jung, Alexander & El-Shagi, Makram & Giesen, Sebastian, 2013. "Does Central Bank Staff Beat Private Forecasters?," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79925, Verein für Socialpolitik / German Economic Association.
  2. Koop, Gary & Onorante, Luca, 2011. "Estimating Phillips Curves in Turbulent Times using the ECB’s Survey of Professional Forecasters," SIRE Discussion Papers 2011-19, Scottish Institute for Research in Economics (SIRE).
  3. D’Agostino, Antonello & Schnatz, Bernd, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series 1455, European Central Bank.
  4. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
  5. Luis E. Rojas, . "Professional Forecasters: How to Understand and Exploit Them Through a DSGE Model," Borradores de Economia 664, Banco de la Republica de Colombia.
  6. Driver, Ciaran & Trapani, Lorenzo & Urga, Giovanni, 2013. "On the use of cross-sectional measures of forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 29(3), pages 367-377.

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