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Estimating Time-Varying Long-Run Growth Rate of Russian GDP in the ARX Model with Oil Prices
[Оценка Траектории Темпов Трендового Роста Ввп России В Arx-Модели С Ценами На Нефть]

Author

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  • Polbin, Andrey V. (Полбин, Андрей В.)

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

Abstract

The paper estimates the path of trend growth rates for Russian GDP based on an autoregressive model with exogenous variables and with a time-varying parameter of trend growth, which, by assumption, is described by a random walk process. In conditions of a high dependence of the Russian economy on commodity exports, terms of trade are used as a control exogenous variable for GDP dynamics. For the purpose of econometric estimation, the ARX model is presented as an unobserved components model and estimated using the maximum likelihood method with the Kalman filter applied. It is shown that in the first half of the 2000s the trend growth rate was at 4%, which can be interpreted as recovery growth after a transformational recession. The higher growth rates actually achieved during this period are explained by the intensive growth of world oil prices. Later, the potential for recovery growth was exhausted, and after the crisis of 2008 the rates of trend growth were remaining at the level of 2% per year for a long period of time. However, following the 2014 crisis, trend growth rates began to decline steadily, and had reached about 1% per year by the beginning of 2019, which can be interpreted as the impact of sanctions and geopolitical uncertainty on the economic development of the Russian Federation. The results of an econometric analysis of the model on household consumption and investment data also suggest that the trend growth rate is approximately 1% per year at present.

Suggested Citation

  • Polbin, Andrey V. (Полбин, Андрей В.), 2020. "Estimating Time-Varying Long-Run Growth Rate of Russian GDP in the ARX Model with Oil Prices [Оценка Траектории Темпов Трендового Роста Ввп России В Arx-Модели С Ценами На Нефть]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 40-63, February.
    • Handle: RePEc:rnp:ecopol:ep2002
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    References listed on IDEAS

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    1. Дубовский Дмитрий Леонидович & Кофанов Дмитрий Александрович & Сосунов Кирилл Александрович, 2015. "Датировка Российского Бизнес-Цикла," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 19(4), pages 554-575.
    2. A. Kudrin & E. Gurvich., 2014. "A New Growth Model for the Russian Economy," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 12.
    3. Glocker, Christian & Wegmueller, Philipp, 2018. "International evidence of time-variation in trend labor productivity growth," Economics Letters, Elsevier, vol. 167(C), pages 115-119.
    4. Kudrin, Alexey & Gurvich, Evsey, 2015. "A new growth model for the Russian economy1," Russian Journal of Economics, Elsevier, vol. 1(1), pages 30-54.
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    Citations

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    Cited by:

    1. Luysmila Kitrar, Tamara Lipkind, 2020. "Analysis Of Interconnection Of The Indicator Of Economic Attitude And Growth [Анализ Взаимосвязи Показателя Экономического Отношения И Роста]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 6, pages 8-41, December.
    2. Zubarev, A. & Rybak, K., 2022. "The impact of global shocks on the Russian economy: FAVAR approach," Journal of the New Economic Association, New Economic Association, vol. 56(4), pages 48-68.
    3. Artur R. Sharafutdinov, 2023. "Output Gap in Russian Economy: Estimate Based on the IMF’s Multivariate Filter [Разрыв Выпуска В Российской Экономике: Оценка На Основе Многомерного Фильтра Мвф]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 4, pages 15-23, April.
    4. Fokin, Nikita, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    5. Artur R. Sharafutdinov, 2023. "Разрыв Выпуска В Российской Экономике: Оценка На Основе Многомерного Фильтра Мвф," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 4, pages 15-23, April.
    6. Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.

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    More about this item

    Keywords

    time-varying parameters model; long-run growth of GDP; Russian economy; oil prices; terms of trade; investment; consumption;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

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