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

Author

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  • Yongchen Zhao

    (Towson University)

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.

Suggested Citation

  • Yongchen Zhao, 2015. "Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms," Working Papers 2015-005, The George Washington University, The Center for Economic Research.
    • Handle: RePEc:gwc:wpaper:2015-005
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    Cited by:

    1. Dean Fantazzini & Erik Nigmatullin & Vera Sukhanovskaya & Sergey Ivliev, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    2. Constantin Bürgi & Tara M. Sinclair, 2017. "A nonparametric approach to identifying a subset of forecasters that outperforms the simple average," Empirical Economics, Springer, vol. 53(1), pages 101-115, August.
    3. Constantin Rudolf Salomo Bürgi, 2023. "How to deal with missing observations in surveys of professional forecasters," Journal of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 2185975-218, December.
    4. Yongchen Zhao, 2020. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 77-97, November.
    5. Dean Fantazzini & Erik Nigmatullin & Vera Sukhanovskaya & Sergey Ivliev, 2017. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. Part 2," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 45, pages 5-28.

    More about this item

    Keywords

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    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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