ESTIMATION OF ECONOMIC GROWTH CONVERGENCE IN EEU COUNTRIES*

The article outlines methodological approaches to measuring the level of convergence in the Eurasian Economic Union (EEU) countries. The authors introduce the concept of conditional cyclical convergence of national economies as a convergence of short-term growth cycles in the overall macroeconomic dynamics. Decomposition of the cyclical macroeconomic dynamics in the countries is performed through identifying a long-term sustainable profile and short-term growth cycles. For this purpose, the double-pass through the Hodrick-Prescott statistical filter is applied for the original time series. Identified short-term growth cycles are visualized for each country using tracers of cyclic profiles. The conditional convergence of economies is determined on the basis of statistically significant cross-correlation coefficients.

The results of the calculations also make it possible to estimate the degree of synchronism in short-term growth cycles in the dynamics of the gross domestic product (GDP) index, the stable trajectories of the countries economic development, and draw a number of conclusions, in particular, to assess the slowdown in long-term macroeconomic growth in recent years and the growth volatility.

A distinctive feature of recent convergence in the economic integration is not the gaps reduction between the countries potentials, but the convergence of short-term growth cycles in the macroeconomic development. The growth slowdown in the Russian economy did not contribute to the positive prospects in the EEA countries. The strongest correlation in the analyzed period describing over 90% of the entire variation in time series was observed in the short-term GDP growth profiles inRussia,Kazakhstan, andBelarus.

Based on the calculations performed, the conclusions are drawn about the large-scale slowdown of long-term stable profiles in the dynamics of macroeconomic growth in all countries of the integration, the noticeable volatility of growth, almost simultaneously observed two years of recession with a clear predominance of major crisis events in 2015.

1. Kitrar L.A. Analiticheskii otchet «Promyshlennoe razvitie v SNG: est li usloviya dlya narashchivaniya potentsiala reindustrializatsii?» [Industrial development in the CIS: Re-industrialization trends and potential]. Moscow, UNIDO, 2017, рр. 15-20. (In Russ.).

2. Tsentr kon”yunkturnykh issledovanii ISIEZ NIU VShE. Indeks ekonomicheskogo nastroeniya (IEN VShE). Ezhekvartal’nye doklady [Centre for Business Tendency Studies ISSEK HSE. Economic Sentiment Indicator (ESI HSE). Quarterly Report]. Moscow, HSE Publ., 2012-2017. (In Russ.). Available at: https://www.hse.ru/monitoring/buscl/bl5.

3. The Central Bank of the Russian Federation. Obzorfinansovoi stabil’nosti. Iyun’ 2014 [Financial Stability Review. June 2014]. Moscow, The Central Bank of the Russian Federation Publ., 2014. (In Russ.).

4. The Central Bank of the Russian Federation. Obzor finansovoi stabil’nosti. 2015. № 2 (II-III kvartaly 2015 g.) [Financial Stability Review, 2015, no. 2 (2015 Q2-Q3)]. Moscow, The Central Bank of the Russian Federation Publ., 2015. (In Russ.).

5. International Monetary Fund. World Economic Outlook: Too slow for too long. Washington, April 2016.

6. Hodrick R.J., Prescott E.C. Postwar U.S. business cycles: An empirical investigation. Journal of Money Credit and Banking, 1997, vol. 29, no. 1, pp. 1-16.

7. Barro R.J. Economic growth in a cross-section of countries. Quarterly Journal of Economics, 1991, vol. 106, pp. 407-443.

8. Hall R.E., Jones Ch.I. Whydo some countries produce so much more output per worker than others? Quarterly Journal of Economics, 1999, vol. 114, pp. 83-116.

9. Mankiw G., Romer D. (eds.). New Keynesian economics. Cambridge, MA, MIT Press. 1991.

10. Nilsson R., Guidetti E. Predicting the business cycle: How good are early estimates of OECD composite leading indicators? Statistics Brief, OECD, 2008, no. 14, pp. 1-14.

11. Kitrar L.A., Lipkind T.M., Ostapkovich G.Y., Chusovlyanov D.s. Praktika identifikatsii nenablyudaemykh komponent traektorii VVP: potentsial’nyi uroven’ i kratkosrochnye razryvy [Identification of the unobservable components in the output trajectory: Potential level and gaps]. Voprosy statistiki, 2015, no. 10, pp. 14-25. (In Russ.).

12. OECD. System of composite leading indicators. April 2012. Available at: http://www.oecd.org/std/leading-indicators/41629509.pdf.

13. Kitrar L., Lipkind T., Ostapkovich G. Dekompozitsiya i sovmestnyi analiz tsiklov rosta v dinamike indikatora ekonomicheskogo nastroeniya i indeksa fizicheskogo ob”ema valovogo vnutrennego produkta [Decomposition and joined analysis of growth cycles in the dynamics of economic sentiment indicator and volume index of the Gross Domestic Product]. Voprosy statistiki, 2014, no. 9, pp. 41-46. (In Russ.).

14. Kitrar L. et al. The HSE ESI and short-term cycles in the Russian economy. Papers and Studies of Research Institute for Economic Development SGH, 2015, no. 97.

15. European Commission. European Business Cycle Indicators - 1st Quarter 2017. Technical Papers 15. Brussels. April 2017.