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Об Использовании Моделей Панельных Данных Для Прогнозирования Темпов Роста Отраслей Российской Обрабатывающей Промышленности

Author

Listed:
  • Andrey V. Polbin

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

  • Andrey V. Shumilov

    (Russian Presidential Academy of National Economy and Public Administration)

Abstract

В работе исследуется вопрос использования моделей панельных данных для прогнозирования роста производства отраслей российской обрабатывающей промышленности. На месячных данных 2015–2021 гг. показано, что одношаговые прогнозы панельных моделей качественно превосходят наивные прогнозы, основанные на усреднении значений роста предыдущих периодов. По сравнению с индивидуальными отраслевыми моделями панельные модели дают более точные прогнозы на 1– 6 месяцев вперед для ряда отраслей, однако совокупная точность прогнозирования при этом улучшается незначительно. Статья подготовлена в рамках выполнения научно-исследовательской работы государственного задания РАНХиГС.

Suggested Citation

  • Andrey V. Polbin & Andrey V. Shumilov, 2022. "Об Использовании Моделей Панельных Данных Для Прогнозирования Темпов Роста Отраслей Российской Обрабатывающей Промышленности," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 2, pages 15-19, February.
    • Handle: RePEc:gai:ruserr:r2215
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    References listed on IDEAS

    as
    1. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
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    3. Eric Girardin & Konstantin A. Kholodilin, 2011. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 622-643, November.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    прогнозирование; динамическая модель панельных данных; рост производства; отрасли обрабатывающей промышленности России;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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