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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

Из многочисленных эмпирических работ следует, что объединение (комбинирование) прогнозов позволяет повысить точность прогнозирования по сравнению с индивидуальными прогнозами. В настоящей статье исследуются возможности регрессионных методов комбинирования прогнозов для улучшения качества прогнозов цен на нефть, алюминий, золото, никель и медь. Основой для расчетов служит база прогнозов Института экономической политики им. Е.Т. Гайдара, предоставляющая массив индивидуальных (объединяемых) прогнозов. Все расчеты проводятся в режиме (псевдо) реального времени. На основе полученных в работе оценок можно утверждать, что для цен на ресурсы независимо от рассматриваемого периода существует регрессионный метод объединения, обеспечивающий качественные преимущества относительно всех первичных прогнозов. Вместе с тем, обобщая результаты качественных характеристик регрессионных и простейших методов комбинирования, следует отметить, что выбор лучшего способа объединения прогнозов (и даже группы способов) неоднозначен и зависит от прогнозируемого показателя.

Suggested Citation

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

    as
    1. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(1), pages 60-68, February.
    2. 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.
    3. 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.
    4. 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.
    5. Leeb, Hannes & Pötscher, Benedikt M., 2008. "Can One Estimate The Unconditional Distribution Of Post-Model-Selection Estimators?," Econometric Theory, Cambridge University Press, vol. 24(2), pages 338-376, April.
    6. 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

    комбинирование прогнозов; объединение прогнозов; цены на нефть; цены на алюминий; цены на золото; цены на никель; цены на медь;
    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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