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Structural change and the combination of forecasts

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  • Francis X. Diebold
  • Peter Pauly

Abstract

Forecasters are generally concerned about the properties of model‐based predictions in the presence of structural change. In this paper, it is argued that forecast errors can under those conditions be greatly reduced through systematic combination of forecasts. We propose various extensions of the standard regression‐based theory of forecast combination. Rolling weighted least squares and time‐varying parameter techniques are shown to be useful generalizations of the basic framework. Numerical examples, based on various types of structural change in the constituent forecasts, indicate that the potential reduction in forecast error variance through these methods is very significant. The adaptive nature of these updating procedures greatly enhances the effect of risk‐spreading embodied in standard combination techniques.

Suggested Citation

  • Francis X. Diebold & Peter Pauly, 1987. "Structural change and the combination of forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 6(1), pages 21-40.
    • Handle: RePEc:wly:jforec:v:6:y:1987:i:1:p:21-40
    DOI: 10.1002/for.3980060103
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    References listed on IDEAS

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    6. Francis X. Diebold & Peter Pauly, 1986. "Structural change and the combination of forecasts," Special Studies Papers 201, Board of Governors of the Federal Reserve System (U.S.).
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    Cited by:

    1. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    2. Xi Wu & Adam Blake, 2023. "Does the combination of models with different explanatory variables improve tourism demand forecasting performance?," Tourism Economics, , vol. 29(8), pages 2032-2056, December.
    3. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Analysis of Opportunities to Improve the Quality of Natural Resource Price by Combining Forecasts Resulting from Methods Based on Regression Estimates of Weights [Анализ Возможностей Улучшения Каче," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    4. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    5. Dejian Yu & Libo Sheng & Shunshun Shi, 2023. "A retrospective analysis of Journal of Forecasting: From 1982 to 2019," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 1008-1035, July.
    6. Heng, Jiani & Wang, Jianzhou & Xiao, Liye & Lu, Haiyan, 2017. "Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting," Applied Energy, Elsevier, vol. 208(C), pages 845-866.
    7. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    8. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Анализ Возможностей Улучшения Качества Прогнозов Цен На Природные Ресурсы Методами Комбинирования На Основе Регрессионных Оценок Весов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.

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