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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).

Suggested Citation

  • Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237, April.
  • Handle: RePEc:eee:intfor:v:27:y::i:2:p:224-237
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
    2. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    3. García-Martos, Carolina & Bastos, Guadalupe & Alonso Fernández, Andrés Modesto, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-21, July.
    5. Constantin Bürgi & Tara M. Sinclair, 2017. "A nonparametric approach to identifying a subset of forecasters that outperforms the simple average," Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
    6. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. repec:kap:jgeosy:v:19:y:2017:i:4:d:10.1007_s10109-017-0259-9 is not listed on IDEAS
    8. Pablo Pincheira & Andrés Gatty, 2016. "Forecasting Chilean inflation with international factors," Empirical Economics, Springer, vol. 51(3), pages 981-1010, November.
    9. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1181-6 is not listed on IDEAS
    10. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    11. Víctor López-Pérez, 2017. "Do professional forecasters behave as if they believed in the New Keynesian Phillips Curve for the euro area?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 147-174, February.

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