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Forecast combination through dimension reduction techniques

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  • Poncela, Pilar
  • Rodríguez, Julio
  • Sánchez-Mangas, Rocío
  • Senra, Eva

Abstract

This paper considers several methods of producing a single forecast from several individual ones. We compare "standard" but hard to beat combination schemes (such as the average of forecasts at each period, or consensus forecast and OLS-based combination schemes) with more sophisticated alternatives that involve dimension reduction techniques. Specifically, we consider principal components, dynamic factor models, partial least squares and sliced inverse regression. Our source of forecasts is the Survey of Professional Forecasters, which provides forecasts for the main US macroeconomic aggregates. The forecasting results show that partial least squares, principal component regression and factor analysis have similar performances (better than the usual benchmark models), but sliced inverse regression shows an extreme behavior (performs either very well or very poorly).

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 2 (April)
Pages: 224-237

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Handle: RePEc:eee:intfor:v:27:y::i:2:p:224-237

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Combining forecasts Factor analysis PLS Principal components SIR Survey of Professional Forecasters;

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  1. Issler, João Victor & Lima, Luiz Renato, 2009. "A panel data approach to economic forecasting: The bias-corrected average forecast," Journal of Econometrics, Elsevier, vol. 152(2), pages 153-164, October.
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  9. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
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  11. Heij, Christiaan & Groenen, Patrick J.F. & van Dijk, Dick, 2007. "Forecast comparison of principal component regression and principal covariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3612-3625, April.
  12. 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.
  13. Forni M. & Hallin M., 2003. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Computing in Economics and Finance 2003 143, Society for Computational Economics.
  14. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
  15. Pena, Daniel & Poncela, Pilar, 2004. "Forecasting with nonstationary dynamic factor models," Journal of Econometrics, Elsevier, vol. 119(2), pages 291-321, April.
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