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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. ⇤ We would like to thank Neil Ericsson, Tatevik Sekhposyan, Herman Stekler and Benjamin Williams for their valuable comments and support. We would also like to thank participants in the Federal Forecasters Conference, the Georgetown Center for Economic Research (GCER) conference, and the GWU SAGE seminar series.

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