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

Yongchen Zhao

Empirical Economics, 2021, vol. 61, issue 1, No 7, 173-199

Abstract: 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.

Keywords: Exponential re-weighting; Shrinkage; Estimation error; Performance instability (search for similar items in EconPapers)
JEL-codes: C15 C22 C53 (search for similar items in EconPapers)
Date: 2021
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Working Paper: Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms (2015)
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DOI: 10.1007/s00181-020-01864-w

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