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Robust model rankings of forecasting performance

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  • Prasad Sankar Bhattacharya
  • Dimitrios D. Thomakos

Abstract

This paper investigates robust model rankings in out‐of‐sample, short‐horizon forecasting. We provide strong evidence that rolling window averaging consistently produces robust model rankings while improving the forecasting performance of both individual models and model averaging. The rolling window averaging outperforms the (ex post) “optimal” window forecasts in more than 50% of the times across all rolling windows.

Suggested Citation

  • Prasad Sankar Bhattacharya & Dimitrios D. Thomakos, 2018. "Robust model rankings of forecasting performance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 676-690, September.
    • Handle: RePEc:wly:jforec:v:37:y:2018:i:6:p:676-690
    DOI: 10.1002/for.2529
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    Cited by:

    1. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    2. Boldyryev, Stanislav & Gil, Tatyana & Ilchenko, Mariia, 2022. "Environmental and economic assessment of the efficiency of heat exchanger network retrofit options based on the experience of society and energy price records," Energy, Elsevier, vol. 260(C).
    3. Zhou, Zhongbao & Gao, Meng & Xiao, Helu & Wang, Rui & Liu, Wenbin, 2021. "Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources," Omega, Elsevier, vol. 104(C).
    4. Llewellyn, Mary & Ross, Gordon & Ryan-Saha, Joshua, 2023. "COVID-era forecasting: Google trends and window and model averaging," Annals of Tourism Research, Elsevier, vol. 103(C).

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