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

Yongchen Zhao

No 2015-005, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting

Abstract: Based on a set of carefully designed Monte Carlo exercises, this paper document the behavior and performance of several newly developed advanced forecast combination algorithms in unstable environments, where performance of candidate forecasts are cross-sectionally heterogeneous and dynamically evolving over time. Results from these exercises provide guidelines regarding the selection of forecast combination method based on the nature, frequency, and magnitude of instabilities in forecasts as well as the target variable. Following these guidelines, a simple forecast combination exercise using the U.S. Survey of Professional Forecasters, where combined forecasters are shown to have superior performance that is not only statistically significant but also of practical importance.

Keywords: Forecast combination; exponential re-weighting; shrinkage; estimation error; performance stability; real-time data (search for similar items in EconPapers)
JEL-codes: C15 C22 C53 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2015-12
New Economics Papers: this item is included in nep-ets, nep-for and nep-pke
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Citations: View citations in EconPapers (4)

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https://www2.gwu.edu/~forcpgm/2015-005.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 (2020)
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