Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms

Yongchen Zhao

No 2015-04, Working Papers from Towson University, Department of Economics

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 combina- tion 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)
Pages: 41 pages
Date: 2015-12, Revised 2020-03
New Economics Papers: this item is included in nep-ecm and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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http://webapps.towson.edu/cbe/economics/workingpapers/2015-04.pdf First version, 2015 (application/pdf)

Related works:
Journal Article: The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms (2021)
Working Paper: Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms (2015)
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