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A macroeconometric model for Russia

Author

Listed:
  • Aizhan Bolatbayeva

    (NAC Analytica, Nur-Sultan, Kazakhstan)

  • Alisher Tolepbergen

    (NAC Analytica, Nur-Sultan, Kazakhstan)

  • Nurdaulet Abilov

    (NAC Analytica, Nur-Sultan, Kazakhstan)

Abstract

The paper outlines a structural macroeconometric model for the economy of Russia. The aim of the research is to analyze how the domestic economy functions, generate forecasts for important macroeconomic indicators and evaluate the responses of main endogenous variables to various shocks. The model is estimated based on quarterly data starting from 2001 to 2019. The majority of the equations are specified in error correction form due to the non-stationarity of variables. Stochastic simulation is used to solve the model for expost and ex-ante analysis. We compare forecasts of the model with forecasts generated by the VAR model. The results indicate that the present model outperforms the VAR model in terms of forecasting GDP growth, inflation rate and unemployment rate. We also evaluate the responses of main macroeconomic variables to VAT rate and world trade shocks via stochastic simulation. Finally, we generate ex-ante forecasts for the Russian economy under the baseline assumptions.

Suggested Citation

  • Aizhan Bolatbayeva & Alisher Tolepbergen & Nurdaulet Abilov, 2020. "A macroeconometric model for Russia," Russian Journal of Economics, ARPHA Platform, vol. 6(2), pages 114-143, June.
  • Handle: RePEc:arh:jrujec:v:6:y:2020:i:2:p:114-143
    DOI: 10.32609/j.ruje.6.47009
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    References listed on IDEAS

    as
    1. Dreger, Christian & Marcellino, Massimiliano, 2007. "A macroeconometric model for the Euro economy," Journal of Policy Modeling, Elsevier, vol. 29(1), pages 1-13.
    2. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    3. Dougherty, Christopher, 2011. "Introduction to Econometrics," OUP Catalogue, Oxford University Press, edition 4, number 9780199567089.
    4. repec:bof:bofitp:urn:nbn:fi:bof-201506091268 is not listed on IDEAS
    5. Klaus Weyerstrass & Daniela Grozea-Helmenstein, 2013. "A Macroeconometric Model for Serbia," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 19(2), pages 85-106, May.
    6. Flint Brayton & Peter A. Tinsley, 1996. "A guide to FRB/US: a macroeconomic model of the United States," Finance and Economics Discussion Series 96-42, Board of Governors of the Federal Reserve System (U.S.).
    7. Borzykh, Olga, 2016. "Bank lending channel in Russia: A TVP-FAVAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 96-117.
    8. Boris B. Demeshev & Oxana A. Malakhovskaya, 2015. "Forecasting Russian Macroeconomic Indicators with BVAR," HSE Working papers WP BRP 105/EC/2015, National Research University Higher School of Economics.
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    Cited by:

    1. Nurdaulet Abilov, 2020. "An Estimated Bayesian DSGE Model for Kazakhstan," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 8(1), pages 30-54, March.

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    More about this item

    Keywords

    macroeconometric model Cowles Commission approach structural macroeconomic model macroeconomic model for Russia forecasting;

    JEL classification:

    • B22 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Macroeconomics
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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