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

Constantin Bürgi and Tara Sinclair

No 2015-006, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting

Abstract: 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.

Keywords: Forecast combination; Forecast evaluation; Multiple model comparisons; Real-time data; Survey of Professional Forecasters (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2015-12
New Economics Papers: this item is included in nep-ecm and nep-for
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https://www2.gwu.edu/~forcpgm/2015-006.pdf First version, 2015 (application/pdf)

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Journal Article: A nonparametric approach to identifying a subset of forecasters that outperforms the simple average (2017) Downloads
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