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Modeling Russian social indicators

Author

Listed:
  • Aivazian, Sergei

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

  • Bereznyatsky, Aleksandr

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

  • Brodsky, Boris

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

Abstract

In this paper one of the methods of social indicators modeling is discussed. We suggest three-stage approach: initially we develop theoretical model of Russian economy, taking into consideration some features such as Dutch disease. The set of exogenous variables are derived from the model. At the second step econometric modeling is used to test the findings. Finally we explore the regional level of the problem in order to rigorously test the models and to explain the outcomes of variety of researches focusing on spatial distribution of social indicators values in the Russian economy.

Suggested Citation

  • Aivazian, Sergei & Bereznyatsky, Aleksandr & Brodsky, Boris, 2018. "Modeling Russian social indicators," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 51, pages 5-32.
  • Handle: RePEc:ris:apltrx:0347
    as

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    References listed on IDEAS

    as
    1. Joseph E. Stiglitz, 2012. "Macroeconomic Fluctuations, Inequality, and Human Development," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 13(1), pages 31-58, February.
    2. Aivazian, Sergei & Bereznyatsky, Aleksandr & Brodsky, Boris, 2017. "Macroeconomic modeling of the Russian economy," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 47, pages 5-27.
    3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    4. Jesús Fernández-Villaverde, 2010. "The econometrics of DSGE models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 1(1), pages 3-49, March.
    5. Aivazian, Sergei & Bereznyatskiy, Alexander & Brodsky, Boris, 2014. "Dutch disease in Russian and Armenian economies," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 36(4), pages 32-60.
    6. David F Hendry & John N J Muellbauer, 2018. "The future of macroeconomics: macro theory and models at the Bank of England," Oxford Review of Economic Policy, Oxford University Press, vol. 34(1-2), pages 287-328.
    7. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
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    More about this item

    Keywords

    Russian economy; disaggregated macromodel; applied econometric analysis; social sector; regional economy.;
    All these keywords.

    JEL classification:

    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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