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Forecasting Output Growth of Russian Manufacturing Industries Using Panel Data Models
[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

In this paper, we utilize panel data models for forecasting output growth rates of Russian manufacturing industries. Using monthly data for 2015–2021, we find that one-month-ahead forecasts of panel models are superior to corresponding naive forecasts based on averaging past growth rates. Compared to individual industry models, panel data models yield better forecasts at 1–6 months horizons for a number of industries, but the overall forecasting accuracy improves only slightly. The article was written on the basis of the RANEPA state assignment research programme.

Suggested Citation

  • Andrey V. Polbin & Andrey V. Shumilov, 2022. "Forecasting Output Growth of Russian Manufacturing Industries Using Panel Data Models [Об Использовании Моделей Панельных Данных Для Прогнозирования Темпов Роста Отраслей Российской Обрабатывающей ," Russian Economic Development, Gaidar Institute for Economic Policy, issue 2, pages 15-19, February.
    • Handle: RePEc:gai:recdev:r2215
    as

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    References listed on IDEAS

    as
    1. Ciaran Driver & Katsushi Imai & Paul Temple & Giovanni Urga, 2004. "The effect of uncertainty on UK investment authorisation: Homogenous vs. heterogeneous estimators," Empirical Economics, Springer, vol. 29(1), pages 115-128, January.
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    4. 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.
    5. 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

    forecasting; dynamic panel data model; output growth; Russian manufacturing industries;
    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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