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Title: Solving the forecast combination puzzle Authors:  Gael Martin - Monash University (Australia) [presenting]
Abstract: The purpose is to demonstrate that the so-called forecasting combination puzzle is a consequence of the methodologies commonly used to produce forecast combinations. By the combination puzzle, we refer to the empirical finding that predictions formed by combining multiple forecasts in ways that seek to optimize forecast performance often do not outperform more naive, e.g. equally-weighted approaches. In particular, we demonstrate that, due to the manner in which such forecasts are typically produced, tests that aim to discriminate between the predictive accuracy of such competing combinations can have low power, and can lack size control, leading to an outcome that favors the simpler approach. In short, we show that this counter-intuitive result can be completely avoided by the adoption of more efficient estimation strategies in the production of the combinations. We illustrate these findings both in the context of forecasting a functional of interest and in terms of predictive densities.