Dynamics Factors and Slow-Response Characteristics of Russian Trade Ties

  • Natalya Yuryevna SOPILKO Peoples' Friendship University of Russia (RUDN University), Moskow
  • Natalia Anatolevna NAVROTSKAIA Saint-Petersburg State University, St. Petersburg
  • Ekaterina Alexandrovna KOVALEVA Peoples' Friendship University of Russia (RUDN University), Moscow
  • Angelika Feliksovna ORLOVA Peoples' Friendship University of Russia (RUDN University), Moscow
  • Anna Vladimirovna GRIGORYEVA Peoples' Friendship University of Russia (RUDN University), Moscow


Transformation of the world economy, new economic challenges, integration processes as a basis for international production cooperation require an in-depth study of international trade development.

The aim of the research is modeling of trade ties of Russia and its key foreign economic partners and distinguishing crucial factors influencing dynamics of external trade flows.

As part of the study we selected appropriate methods of econometric analysis of international trade dynamics in various branches. We used the following methods: a gravity model of mutual trade, a method of regressive decision trees, autoregression and a method of multidimensional adaptive regressive splines. We also distinguished crucial exogenous factors, influencing mutual trade of the researched countries. Having used various models, we found the most important determining factor for Russian foreign trade – prior period export (a system of international contracts). It can be explained by high transaction costs connected with new business- partners search and predominance of large long – term contracts in international trade.

Using the concept of ‘specificity of assets’, we substantiated a response rate of international trade ties, which tend to be reproduced in former structural parameters as time goes by. It was proved that a response rate of international trade ties is characteristic for all researched countries.


[1] Bergeijk, P., and Brakman, S. 2009. The Gravity Model in International Trade. Advances and Applications. Cambridge: Cambridge University Press.
[2] Bevan, A., and Estrin, S. 2004. The determinants of foreign direct investment into European transition economies. Journal of Comparative Economics, 3: 775–787.
[3] Breiman, L., Friedman, J., Olshen, R., and Stone, S. 1984. Classification and Regression Trees. Belmont: Wadsworth International Group.
[4] Chian, A. 2007. Complex Systems Approach to Economic Dynamics. Berlin: Springer.
[5] Deardorff, A. 1998. Determinants of Bilateral Trade: Does Gravity Work in a Neoclassical World in The Regionalization of World Economy. Chicago: Chicago University Press, pp. 7–32.
[6] Dobra, A., and Gehrke, J. 2002. SECRET: A scalable linear regression tree algorithm. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press.
[7] Frankel, J. 1997. Regional Trading Blocs. Washington: Institute for International Economics
[8] Freinkman, L., and Dashkeev V. 2009. Analiz institutsional'noi dinamiki v stranakh s perekhodnoi ekonomikoi [An analysis of institutional dynamics in countries with economies in transition]. Мoscow: IEPP.
[9] Friedman, J. 1991. Multivariate adaptive regression splines. The Annals of Statistics, 19(1): 1–141.
[10] Hastie, T., and Tibshirani, R. 2009. The Elements of Statistical Learning. New York: Springer Series in Statistics.
[11] Helpman, E., and Krugman, Р. 1987. Market Structure and Foreign Trade. Cambridge, Massachusetts: MIT Press, pp. 283.
[12] Holmes, G., and Hall, M. 1999. Generating rule sets from model trees. 12th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 6-10, pp. 1–12.
[13] Kuznetsov, A. 2008. Pryamiye inostranniye investitsii: effekt sosedstva [Foreign direct investments: neighbourhood effect]. World Economy and International Relations, 52(9): 40-47.
[14] Loh, W.-Y. 2002. Regression trees with unbiased variable selection and interaction detection. Statistica Sinica, 12: 361–386.
[15] Navrotskaya, N. 2014. Globalizatsiya investitsionnogo protsessa [The globalization of the investment process]. Dnipropetrovsk: Lira.
[16] Olivero, M., and Yotov Y. 2012. Dynamic gravity: endogenous country size and asset accumulation. Canadian Journal of Economics, 45(1): 64–92.
[17] Redding, S., and Venables, A. 2004. Economic Geography and International Inequality. Journal of International Economics, 62: 53–82.
[18] Yerlikaya, F. 2008. A new contribution to nonlinear robust regression and classification with MARS and its applications to data mining for quality control in manufacturing. MSc Thesis at Institute of Applied Mathematics of METU, Ankara, pp. 1–102.
[19] Wang, Y., and Witten, H. 1997. Induction of model trees for predicting continuous classes. Proceedings European Conference on Machine Learning. Prague: University of Economics, Faculty of Informatics and Statistics, pp. 128–137.
[20] Williamson, O. 2002. The Theory of the Firm as Governance Structure: From Choice to Contract. Journal of Economic Perspectives, 16(3): 171–195.
[21] Williamson, O. 1993. The Nature of the Firm: Origins, Evolution, and Development. Oxford: Oxford University Press.
How to Cite
SOPILKO, Natalya Yuryevna et al. Dynamics Factors and Slow-Response Characteristics of Russian Trade Ties. Journal of Advanced Research in Law and Economics, [S.l.], v. 8, n. 2, p. 625 - 634, aug. 2017. ISSN 2068-696X. Available at: <https://journals.aserspublishing.eu/jarle/article/view/1343>. Date accessed: 20 apr. 2024.