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Design of Methods for Long-Term Forecasting of Development Trends in the Russian Economy (Methodology and Model Toolkit)

Author

Listed:
  • N. V. Suvorov

    (Institute of Economic Forecasting, Russian Academy of Sciences)

  • S. V. Treshchina

    (Institute of Economic Forecasting, Russian Academy of Sciences)

  • Yu. V. Beletskii

    (Institute of Economic Forecasting, Russian Academy of Sciences)

Abstract

The subject of this article is to describe possible areas for improvement of long-term economic forecasting methodology based on the mathematical toolkit of econometrics. Design issues are discussed with regard to methodological principles of applying this toolkit to forecasting and analytics. Special attention is paid to the development of a forecasting and analytical calculation scheme that ensures interconnected generation of long-term indicators of economic development at the macroeconomic and sectoral levels.

Suggested Citation

  • N. V. Suvorov & S. V. Treshchina & Yu. V. Beletskii, 2020. "Design of Methods for Long-Term Forecasting of Development Trends in the Russian Economy (Methodology and Model Toolkit)," Studies on Russian Economic Development, Springer, vol. 31(6), pages 636-646, November.
    • Handle: RePEc:spr:sorede:v:31:y:2020:i:6:d:10.1134_s107570072006012x
    DOI: 10.1134/S107570072006012X
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    References listed on IDEAS

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    1. Burren, Daniel & Neusser, Klaus, 2013. "The Role Of Sectoral Shifts In The Decline Of Real Gdp Volatility," Macroeconomic Dynamics, Cambridge University Press, vol. 17(3), pages 477-500, April.
    2. Adina Ardelean & Miguel Leon-Ledesma & Laura Puzzello, 2017. "Industry Volatility and International Trade," Studies in Economics 1709, School of Economics, University of Kent.
    3. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    4. Danilo Leiva-Leon & Lorenzo Ductor, 2019. "Fluctuations in Global Macro Volatility," ThE Papers 19/09, Department of Economic Theory and Economic History of the University of Granada..
    5. Lauretta, Eliana, 2018. "The hidden soul of financial innovation: An agent-based modelling of home mortgage securitization and the finance-growth nexus," Economic Modelling, Elsevier, vol. 68(C), pages 51-73.
    6. Simón Sosvilla-Rivero & María del Carmen Ramos-Herrera, 2018. "Inflation, real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters," Applied Economics, Taylor & Francis Journals, vol. 50(42), pages 4540-4555, September.
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    Cited by:

    1. N. V. Suvorov & S. V. Treshchina & Yu. V. Beletsky, 2021. "A Study of the Connection between Intertemporal Changes in Consolidated Macroeconomic Indicators and the Performance of Individual Industries in Russia’s Economy," Studies on Russian Economic Development, Springer, vol. 32(6), pages 611-618, November.

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