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Solving the Forecast Combination Puzzle

Author

Listed:
  • David T. Frazier
  • Ryan Covey
  • Gael M. Martin
  • Donald S. Poskitt
Abstract
The forecast combination puzzle is the commonly encountered empirical result whereby predictions formed by combining multiple forecasts in complex ways do not out-perform more naive, e.g. equally-weighted, approaches. While various solutions for the cause of the puzzle exist in the literature, these solutions are limited in their scope and applicability. In contrast, we demonstrate a general solution to the puzzle by showing that this phenomenon is a direct consequence of the methodology used to produce forecast combinations. In particular, we show that tests which aim to discriminate between the predictive accuracy of competing forecast combination strategies have low power, and can lack size control, leading to an outcome that favours the naive approach. In addition, we demonstrate that the low power of such predictive accuracy tests in the forecast combination setting can be completely avoided if more efficient strategies are used 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. A short empirical example using daily financial returns exemplifies how researchers can avoid the puzzle in practical settings.

Suggested Citation

  • David T. Frazier & Ryan Covey & Gael M. Martin & Donald S. Poskitt, 2023. "Solving the Forecast Combination Puzzle," Monash Econometrics and Business Statistics Working Papers 18/23, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2023-18
    as

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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/2023/wp18-2023.pdf
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    References listed on IDEAS

    as
    1. Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
    2. Jeremy Smith & Kenneth F. Wallis, 2009. "A Simple Explanation of the Forecast Combination Puzzle," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 331-355, June.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).

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    More about this item

    Keywords

    optimal forecast combinations; tests for forecast accuracy; probabilistic forecasting; scoring rules; SℰP500 forecasting; one-step versus two-step estimation;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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