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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 Качества Прогнозов Цен На Природные Ресурсы Методами Комбинирования На Основе Регрессионных Оценок Весов]

Author

Listed:
  • Ekaterina V. Astafyeva

    (Russian Presidential Academy of National Economy and Public Administration)

  • Maria Yu. Turuntseva

    (Russian Presidential Academy of National Economy and Public Administration; Gaidar Institute for Economic Policy)

Abstract

Numerous empirical works indicate that combining (aggregation) forecasts can improve forecast accuracy compared to individual forecasts. This paper investigates the possibilities of regression methods of forecast aggregation to improve the forecast quality of prices for oil, aluminum, gold, nickel and copper. The calculations are based on the forecast database of the Gaidar Institute for Economic Policy. E.T. Gaidar Institute for Economic Policy, which provides an array of individual (aggregated) forecasts. All calculations are performed in (pseudo) real time. Based on the findings obtained in the paper, it can be argued that for resource prices, regardless of the period under consideration, there is a regression method of aggregation that provides qualitative advantages relative to all primary forecasts. At the same time, summarizing the results of qualitative characteristics of regression and simple aggregation methods, it should be noted that the choice of the best method of combining forecasts (and even a group of methods) is ambiguous and depends on the indicator.

Suggested Citation

  • 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.
  • Handle: RePEc:gai:recdev:r23100
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    References listed on IDEAS

    as
    1. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 33-41, January.
    2. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(1), pages 60-68, February.
    3. Elliott, Graham & Timmermann, Allan, 2004. "Optimal forecast combinations under general loss functions and forecast error distributions," Journal of Econometrics, Elsevier, vol. 122(1), pages 47-79, September.
    4. 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.
    5. Diebold, Francis X, 1988. "Serial Correlation and the Combination of Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 105-111, January.
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    7. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
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    More about this item

    Keywords

    forecasts combination; forecast aggregation; oil prices; aluminum prices; gold prices; nickel prices; copper prices;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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