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A Nonparametric Approach to Identifying a Subset of Forecasters that Outperforms the Simple Average

Listed author(s):
  • Constantin Bürgi

    ()

    (The George Washington University)

  • Tara M. Sinclair

    ()

    (The George Washington University)

Empirical studies in the forecast combination literature have shown that it is notoriously di!cult to improve upon the simple average despite the availability of optimal combination weights. In particular, historical performance-based combination approaches do not select forecasters that improve upon the simple average going forward. This paper shows that this is due to the high correlation among forecasters, which only by chance causes some individuals to have lower root mean squared errors (RMSE) than the simple average. We introduce a new nonparametric approach to eliminate forecasters who perform well based purely on chance as well as poor performers. This leaves a subset of forecasters with better performance in subsequent periods. It improves upon the simple average in the SPF for bond yields where some forecasters may be more likely to have specialized knowledge.

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File URL: https://www2.gwu.edu/~forcpgm/2015-006.pdf
File Function: First version, 2015
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Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2015-006.

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Length: 21 pages
Date of creation: Dec 2015
Handle: RePEc:gwc:wpaper:2015-006
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  14. 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.
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  16. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Density characteristics and density forecast performance: a panel analysis," Empirical Economics, Springer, vol. 48(3), pages 1203-1231, May.
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  18. 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.
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