IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0347.html
   My bibliography  Save this article

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

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2018_51_005-032.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John J. Heim, 2017. "An Econometric Model of the US Economy," Springer Books, Springer, number 978-3-319-50681-4, July.
    2. 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 and Oxford Review of Economic Policy Limited, vol. 34(1-2), pages 287-328.
    3. James G. MacKinnon, 2010. "Critical Values For Cointegration Tests," Working Paper 1227, Economics Department, Queen's University.
    4. 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.
    5. 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.
    6. 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.
    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.
    8. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Francesco Sergi, 2020. "The Standard Narrative about DSGE Models in Central Banks’ Technical Reports," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 27(2), pages 163-193, March.
    3. Muellbauer, John, 2018. "The Future of Macroeconomics," INET Oxford Working Papers 2018-10, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    4. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
    5. Bekiros, Stelios D. & Paccagnini, Alessia, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 298-323.
    6. Stelios D. Bekiros & Alessia Paccagnini, 2016. "Policy‐Oriented Macroeconomic Forecasting with Hybrid DGSE and Time‐Varying Parameter VAR Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(7), pages 613-632, November.
    7. Alessia Paccagnini, 2012. "Comparing Hybrid DSGE Models," Working Papers 228, University of Milano-Bicocca, Department of Economics, revised Dec 2012.
    8. Gulan, Adam, 2018. "Paradise lost? A brief history of DSGE macroeconomics," Bank of Finland Research Discussion Papers 22/2018, Bank of Finland.
    9. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.
    10. Gary Koop & M. Hashem Pesaran & Ron P. Smith, 2013. "On Identification of Bayesian DSGE Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 300-314, July.
    11. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks´ Macro Models," Working Paper Series 323, Sveriges Riksbank (Central Bank of Sweden).
    12. Jordi Galí & Mark Gertler, 2007. "Macroeconomic Modeling for Monetary Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 21(4), pages 25-46, Fall.
    13. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    14. Lim, G.C. & McNelis, Paul D., 2008. "Computational Macroeconomics for the Open Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262123061, April.
    15. Lucas Papademos, 2005. "Macroeconomic theory and monetary policy: the contributions of Franco Modigliani and the ongoing debate," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 58(233-234), pages 187-214.
    16. Zadrozny, Peter A., 2022. "Linear identification of linear rational-expectations models by exogenous variables reconciles Lucas and Sims," CFS Working Paper Series 682, Center for Financial Studies (CFS).
    17. Signe Rosenberg, 2015. "The Impact of a Change in Real Estate Value on Private Consumption in Estonia," Research in Economics and Business: Central and Eastern Europe, Tallinn School of Economics and Business Administration, Tallinn University of Technology, vol. 7(2).
    18. Ida Wolden Bache, 2008. "Assessing estimates of the exchange rate pass-through," Working Paper 2007/12, Norges Bank.
    19. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.
    20. Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:apltrx:0347. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.