IDEAS home Printed from https://ideas.repec.org/a/scn/accntn/y2019i3p32-42.html
   My bibliography  Save this article

Применение метода попарных сравнений при объединении экономических прогнозов // Application of the Method of Pairwise Comparisons When Combining Economic Forecasts

Author

Listed:
  • A. Surkov A.

    (Financial University)

  • А. Сурков А.

    (Финансовый университет)

Abstract

The method of combining forecasts has already proven itself in practice as a reliable and effective way to improve the accuracy of economic forecasting. But this technique has several disadvantages. Today, one of the ways to improve the method of combining forecasts is to find the possibility of attracting expert information as a tool for correcting the obtained forecast results. This article is devoted to the use of an expert method of pairwise comparisons for constructing the weights of the combined forecast as one of the options for which you can use expert information when combining forecasts. The proposed methodology has been applied in practice for the economic time series of some products of industrial production in Russia. An assessment was made of the effectiveness of using the method of pairwise comparisons for combining forecasts, and based on the results obtained, a forecast of the development of the economic indicators under consideration was proposed. Методика объединения прогнозов, зарекомендовавшая себя на практике как надежный и эффективный способ повысить точность экономического прогнозирования, имеет и ряд недостатков. Сегодня одним из направлений совершенствования методики объединения прогнозов является поиск возможности привлечения экспертной информации как инструмента по корректировке полученных прогнозных результатов. Настоящая статья посвящена применению экспертного метода попарных сравнений для построения весовых коэффициентов объединенного прогноза как один из вариантов, при котором можно использовать экспертную информацию при объединении прогнозов. Предлагаемая методика применена на практике для экономических временных рядов некоторых продуктов промышленного производства России. На основе оценки эффективности применения метода попарных сравнений для объединения прогнозов и полученных результатов предложен прогноз развития рассматриваемых экономических показателей.

Suggested Citation

  • A. Surkov A. & А. Сурков А., 2019. "Применение метода попарных сравнений при объединении экономических прогнозов // Application of the Method of Pairwise Comparisons When Combining Economic Forecasts," Учет. Анализ. Аудит // Accounting. Analysis. Auditing, ФГОБУВО "Финансовый университет при Правительстве Российской Федерации" // Financial University under The Government of Russian Federation, vol. 6(3), pages 32-42.
  • Handle: RePEc:scn:accntn:y:2019:i:3:p:32-42
    as

    Download full text from publisher

    File URL: https://accounting.fa.ru/jour/article/viewFile/255/250.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Claeskens, Gerda & Magnus, Jan R. & Vasnev, Andrey L. & Wang, Wendun, 2016. "The forecast combination puzzle: A simple theoretical explanation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 754-762.
    2. Gupta, Sunil & Wilton, Peter C, 1988. "Combination of Economic Forecasts: An Odds-Matrix Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 373-379, July.
    3. 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. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    2. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    3. Chuanhua Wei & Chenping Du & Nana Zheng, 2020. "A Changing Weights Spatial Forecast Combination Approach with an Application to Housing Price Prediction," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(4), pages 1-11, April.
    4. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    5. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    6. Sebastian M. Blanc & Thomas Setzer, 2020. "Bias–Variance Trade-Off and Shrinkage of Weights in Forecast Combination," Management Science, INFORMS, vol. 66(12), pages 5720-5737, December.
    7. Tae-Hwy Lee & Millie Yi Mao & Aman Ullah, 2021. "Estimation of high-dimensional dynamic conditional precision matrices with an application to forecast combination," Econometric Reviews, Taylor & Francis Journals, vol. 40(10), pages 905-918, November.
    8. Matsypura, Dmytro & Thompson, Ryan & Vasnev, Andrey L., 2018. "Optimal selection of expert forecasts with integer programming," Omega, Elsevier, vol. 78(C), pages 165-175.
    9. 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.
    10. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
    11. Martin, Gael M. & Loaiza-Maya, Rubén & Maneesoonthorn, Worapree & Frazier, David T. & Ramírez-Hassan, Andrés, 2022. "Optimal probabilistic forecasts: When do they work?," International Journal of Forecasting, Elsevier, vol. 38(1), pages 384-406.
    12. Kira Alhorn & Holger Dette & Kirsten Schorning, 2021. "Optimal Designs for Model Averaging in non-nested Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 745-778, August.
    13. Li Liu & Zhiyuan Pan & Yudong Wang, 2021. "What can we learn from the return predictability over the business cycle?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 108-131, January.
    14. Pan, Zhiyuan & Pettenuzzo, Davide & Wang, Yudong, 2020. "Forecasting stock returns: A predictor-constrained approach," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 200-217.
    15. James Younker, 2022. "Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models," Discussion Papers 2022-19, Bank of Canada.
    16. Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022. "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, vol. 301(1), pages 180-190.
    17. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    18. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.
    19. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    20. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.

    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:scn:accntn:y:2019:i:3:p:32-42. 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: Алексей Скалабан (email available below). General contact details of provider: http://accounting.fa.ru .

    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.