Subject The article considers the real GDP growth in Russia within the projected long-term period and analyzes its drivers. Objectives The aim of the study is to identify reasons for projected long-term growth of Russia's real GDP and to link it up with the ontology through the understanding of changes in economic systems. Methods The study employs the systems approach, using the techniques of statistical, cluster, and neural network analysis. Results We identified various systems to assess changes in the GDP of Russia that enable to show its rise and fall. Changes in the nominal GDP of Russia used by the Bank of Russia as a criterion for its assessment make it possible to rationalize the validity of the inflation targeting policy. The combination of inflation targeting and the ‘deleverage’ mechanism applied by the Bank of Russia creates conditions for share price growth in the face of slow pace of investment growth and falling domestic demand. The findings may be helpful for explaining the reasons for changes in the nominal and real GDP of Russia, as well as for educational process. Conclusions The reasons for the forecast volatility of long-term growth of Russia's real GDP include the targeted reduction of inflation and leverage that caused an increase in stock price in the context of slow investment growth and a fall in domestic demand.
Gibson H.D., Hall S.G., Tavlas G.S. Nonlinear Forecast Combinations: An Example Using Euro-Area Real GDP Growth. Journal of Economic Behavior & Organization. (In press, corrected proof). URL: Link
Plante M., Richter A.W., Throckmorton N.A. The Zero Lower Bound and Endogenous Uncertainty. The Economic Journal, 2018, vol. 128, iss. 611, pp. 1730–1757. URL: Link
Reinhart C.M., Reinhart V.R. Author Notes. Financial Crises, Development, and Growth: A Long-term Perspective. The World Bank Economic Review, 2015, vol. 29, iss. suppl_1, pp. S53–S76. URL: Link
Glaz'ev S.Yu. [Stabilization of the monetary and financial market as a necessary condition for the transition to sustainable development]. Ekonomika regiona = Economy of Region, 2016, vol. 12, no. 1, pp. 28–36. URL: Link (In Russ.)
Glaz'ev S.Yu. [Priorities of the Russian economy's accelerated development during the transition to a new technological mode]. Ekonomicheskoe vozrozhdenie Rossii = Economic Revival of Russia, 2019, no. 2, pp. 12–16. URL: Link (In Russ.)
Ivanter V.V. [Prospects of economic growth in Russia]. Nauchnye trudy Vol'nogo ekonomicheskogo obshchestva Rossii = Scientific Works of the Free Economic Society of Russia, 2015, vol. 196, no. 7, pp. 195–202. (In Russ.)
Maevskii V.I. [Mesolevel and hierarchical structure of the economy]. Journal of Institutional Studies, 2018, vol. 10, no. 3, pp. 18–29. (In Russ.) URL: Link
Makarov V.L., Bakhtizin A.R., Khabriev B.R. [Performance evaluation of the mechanisms strengthening the State sovereignty of Russia]. Finansy: teoriya i praktika = Finance: Theory and Practice, 2018, vol. 22, no. 5, pp. 6–26. (In Russ.) URL: Link
Keister T. The Interplay Between Liquidity Regulation, Monetary Policy Implementation and Financial Stability. Global Finance Journal, 2019, vol. 39, pp. 30–38. URL: Link
De Moraes C.O., Montes G.C., Antunes J.A.P. How Does Capital Regulation React to Monetary Policy? New Evidence on the Risk-Taking Channel. Economic Modelling, 2016, vol. 56, pp. 177–186. URL: Link
Tayler W.J., Zilberman R. Macroprudential Regulation, Credit Spreads and the Role of Monetary Policy. Journal of Financial Stability, 2016, vol. 26, pp. 144–158. URL: Link
Lazar J., Feng J.H., Hochheiser H. Chapter 4: Statistical Analysis. In: Research Methods in Human Computer Interaction (Second Edition). Elsevier, 2017, pp. 71–104. URL: Link
Schofield S. Impressive Statistical Analysis. Science and Public Policy, 1993, vol. 20, iss. 3, pp. 214–215. URL: Link
Adolfsson A., Ackerman M., Brownstein N.C. To Cluster, or Not to Cluster: An Analysis of Clusterability Methods. Pattern Recognition, 2019, vol. 88, pp. 13–26. URL: Link
Favero L.P., Belfiore P. Chapter 11: Cluster Analysis. In: Data Science for Business and Decision Making. Elsevier, 2018, pp. 311–382. URL: Link