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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.

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Bibliographic Info

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

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

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Handle: RePEc:eee:intfor:v:27:y:2011: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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References

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Citations

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Cited by:
  1. Pablo Pincheira & Andrés Gatty, 2014. "Forecasting Chilean Inflation with International Factors," Working Papers Central Bank of Chile 723, Central Bank of Chile.
  2. 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.
  3. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
  4. 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.

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