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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

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

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References

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  1. Nolte, Ingmar & Pohlmeier, Winfried, 2007. "Using forecasts of forecasters to forecast," International Journal of Forecasting, Elsevier, vol. 23(1), pages 15-28.
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  5. 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.
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  7. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
  8. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
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  11. Pena, Daniel & Poncela, Pilar, 2004. "Forecasting with nonstationary dynamic factor models," Journal of Econometrics, Elsevier, vol. 119(2), pages 291-321, April.
  12. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  13. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  14. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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Citations

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Cited by:
  1. Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," HSC Research Reports HSC/14/09, Hugo Steinhaus Center, Wroclaw University of Technology.
  2. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
  3. Pablo Pincheira & Andrés Gatty, 2014. "Forecasting Chilean Inflation with International Factors," Working Papers Central Bank of Chile 723, Central Bank of Chile.
  4. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2014. "Selecting and combining experts from survey forecasts," Statistics and Econometrics Working Papers ws140905, Universidad Carlos III, Departamento de Estadística y Econometría.

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