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The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms

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  • Yongchen Zhao

    (Towson University)

Abstract

In this paper, we study the behavior and effectiveness of several recently developed forecast combination algorithms in simulated unstable environments, where the performances of individual forecasters are cross-sectionally heterogeneous and dynamically evolving. Our results clearly reveal how different algorithms respond to structural instabilities of different origin, frequency, and magnitude. Accordingly, we propose an improved forecast combination procedure and demonstrate its effectiveness in a real-time forecast combination exercise using the U.S. Survey of Professional Forecasters.

Suggested Citation

  • Yongchen Zhao, 2021. "The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms," Empirical Economics, Springer, vol. 61(1), pages 173-199, July.
    • Handle: RePEc:spr:empeco:v:61:y:2021:i:1:d:10.1007_s00181-020-01864-w
    DOI: 10.1007/s00181-020-01864-w
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    More about this item

    Keywords

    Exponential re-weighting; Shrinkage; Estimation error; Performance instability;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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