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Forecast Pooling for European Macroeconomic Variables

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  • Massimiliano Marcellino

Abstract

We compare alternative forecast pooling methods and 58 forecasts from linear, time-varying and non-linear models, using a very large dataset of about 500 macroeconomic variables for the countries in the European Monetary Union. On average, combination methods work well but single non-linear models can outperform them for several series. The performance of pooled forecasts, and of non-linear models, improves when focusing on a subset of unstable series, but the gains are minor. Finally, on average over the EMU countries, the pooled forecasts behave well for industrial production growth, unemployment and inflation, but they are often beaten by non-linear models for each country and variable. Copyright 2004 Blackwell Publishing Ltd.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics & Statistics.

Volume (Year): 66 (2004)
Issue (Month): 1 (02)
Pages: 91-112

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Handle: RePEc:bla:obuest:v:66:y:2004:i:1:p:91-112

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Cited by:
  1. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," Ifo Working Paper Series Ifo Working Paper No. 57, Ifo Institute for Economic Research at the University of Munich.
  2. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics, University of Munich, Department of Economics 11140, University of Munich, Department of Economics.
  3. Giancarlo Lutero & Marco Marini, 2010. "Direct vs Indirect Forecasts of Foreign Trade Unit Value Indices," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(2-3), pages 73-96, October.
  4. Katja Drechsel & Laurent Maurin, 2011. "Flow of conjunctural information and forecast of euro area economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 30(3), pages 336-354, April.
  5. MOULIN, Laurent & SALTO, Matteo & SILVESTRINI, Andrea & VEREDAS, David, 2004. "Using intra annual information to forecast the annual state deficits : the case of France," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2004048, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, Elsevier, vol. 24(C), pages 1-24.
  7. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2014. "Selecting and combining experts from survey forecasts," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de Estadística y Econometría ws140905, Universidad Carlos III, Departamento de Estadística y Econometría.

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