IDEAS home Printed from https://ideas.repec.org/a/gai/ruserr/r23100.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.iep.ru/files/RePEc/gai/ruserr/r23100.pdf
    Download Restriction: no
    ---><---

    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. 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.
    3. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(1), pages 60-68, February.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    4. Elkin Castaño & Luis Fernando Melo, 1998. "Métodos de Combinación de Pronósticos: Una Aplicación a la Inflación Colombiana," Borradores de Economia 109, Banco de la Republica de Colombia.
    5. Ekrem Kilic, 2005. "Forecasting Volatility of Turkish Markets: A Comparison of Thin and Thick Models," Econometrics 0510007, University Library of Munich, Germany.
    6. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    7. Guidolin, Massimo & Timmermann, Allan, 2009. "Forecasts of US short-term interest rates: A flexible forecast combination approach," Journal of Econometrics, Elsevier, vol. 150(2), pages 297-311, June.
    8. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    9. 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.
    10. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.
    11. Chanont Banternghansa & Michael W. McCracken, 2011. "Real-time forecast averaging with ALFRED," Review, Federal Reserve Bank of St. Louis, vol. 93(Jan), pages 49-66.
    12. David E. Rapach & Jack K. Strauss, 2005. "Forecasting employment growth in Missouri with many potentially relevant predictors: an analysis of forecast combining methods," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 97-112.
    13. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    14. Jan R. Magnus & Wendun Wang & Xinyu Zhang, 2016. "Weighted-Average Least Squares Prediction," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1040-1074, June.
    15. Jakub Nowotarski, 2013. "Short-term forecasting of electricity spot prices using model averaging (Krótkoterminowe prognozowanie spotowych cen energii elektrycznej z wykorzystaniem uśredniania modeli)," HSC Research Reports HSC/13/17, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    16. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29, January.
    17. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
    18. Pablo Pincheira-Brown & Andrea Bentancor & Nicolás Hardy, 2023. "An Inconvenient Truth about Forecast Combinations," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    19. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    20. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gai:ruserr:r23100. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Olga Beloborodova The email address of this maintainer does not seem to be valid anymore. Please ask Olga Beloborodova to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/gaidaru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.