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Macroeconomic modeling of the Russian economy

Author

Listed:
  • Sergei Aivazian

    (Central Economics and Mathematics Institute of the Russian Academy of Sciences, Russia, Moscow)

  • Aleksandr Bereznyatsky

    (Central Economics and Mathematics Institute of the Russian Academy of Sciences, Russia, Moscow)

  • Boris Brodsky

    (Central Economics and Mathematics Institute of the Russian Academy of Sciences, Russia, Moscow;)

Abstract

In this paper the methodology for disaggregated macroeconomic model of the Russian economy of 1990–2010s is given. In this model the following main sectors of the Russian economy are considered: the real sector (subsectors EOM (export-oriented markets), DOM (domestic oriented markets), EM (natural monopolies)), the financial sector, the population. We try to explain why the real understanding of trends and tendencies of the Russian economy is achieved only via interactions between these sectors. The choice of predictors of the macroeconometric model is carried out on the basis of conclusions of the theoretical disaggregated model: the main factors of long-term dynamics in co-integrated equations are considered and the functional form of these equations is chosen. The main conclusion of the authors: the theoretical description of the Russian economy is possible on the basis of the structural disaggregated model which can be used for the macroeconometric modeling.

Suggested Citation

  • Sergei Aivazian & Aleksandr Bereznyatsky & Boris Brodsky, 2017. "Macroeconomic modeling of the Russian economy," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 47, pages 5-27.
    • Handle: RePEc:ris:apltrx:0322
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    References listed on IDEAS

    as
    1. Gomes, S. & Jacquinot, P. & Pisani, M., 2012. "The EAGLE. A model for policy analysis of macroeconomic interdependence in the euro area," Economic Modelling, Elsevier, vol. 29(5), pages 1686-1714.
    2. Daniel M. Rees & Penelope Smith & Jamie Hall, 2016. "A Multi-sector Model of the Australian Economy," The Economic Record, The Economic Society of Australia, vol. 92(298), pages 374-408, September.
    3. Perron, P, 1993. "Erratum [The Great Crash, the Oil Price Shock and the Unit Root Hypothesis]," Econometrica, Econometric Society, vol. 61(1), pages 248-249, January.
    4. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
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    Cited by:

    1. Sergei Aivazian & Aleksandr Bereznyatsky & Boris Brodsky, 2018. "Modeling Russian social indicators," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 51, pages 5-32.
    2. Айвазян С.А. & Березняцкий А.Н.* & Бродский Б.Е.**, 2019. "Неравновесные Структурные Модели Реального Сектора Российской Экономики," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(2), pages 65-80, апрель.

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    More about this item

    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - 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
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical

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