Perova V.I.National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation perova_vi@mail.ru
Lastochkina E.I.National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation mmep@iee.unn.ru
Importance The article considers the changes in and characteristics of the investment activities and behavior of the Russian Federation regions. Objectives The article aims to analyze and describe the trends and characteristics of fixed capital investment behavior of the Russian Federation regions to ensure the economic growth and socio-economic development of the country and regions. Methods We examine the regions' investment activities for the period from 2012 through 2014 using the neural modeling methodology on the basis of thirteen indicators characterizing the investment activities of the regions and defining their socio-economic development prospects. We also apply the Self Organizing Map using the STATISTICA software. Data of the Federal State Statistics Service of Russia on fixed investment by type of economic activity in the regions underlie our study. Results The paper shows certain characteristics and peculiarities of the investment performance and behavior of the Russian Federation regions. Conclusions and Relevance The cluster analysis of the Russian Federation regions' investment activities shows their uneven nature. The findings indicate the need for comprehensive measures to help change the structure of the investments involved and stimulate investment activity in all regions of the Russian Federation.
Soboleva I.V. [Paradoxes of human capital measurement]. Voprosy Ekonomiki, 2009, no. 9, pp. 51–70. (In Russ.)
Soboleva I. Paradoxes of the Measurement of Human Capital. Problems of Economic Transition, 2010, vol. 52, no. 11, pp. 43–70.
Kuznetsov Yu.A., Michasova O.V. [Formalization of the task of identifying and analyzing the main economic growth determinants in Russia]. Vestnik Nizhegorodskogo universiteta im. N.I. Lobachevskogo. Ser. Sotsial'nye nauki = Vestnik of Lobachevsky State University of Nizhny Novgorod. Ser. Social Sciences, 2015, no. 3, pp. 9–19. (In Russ.) URL: Link
Kuznetsov Yu.A., Umilina A.Yu. [Some features of economic growth in developing countries and a mathematical model of economic growth of the Nelson–Phelps type]. Vestnik Nizhegorodskogo universiteta im. N.I. Lobachevskogo. Ser. Sotsial'nye nauki = Vestnik of Lobachevsky State University of Nizhny Novgorod. Ser. Social Sciences, 2015, no. 4, pp. 36–44. (In Russ.) URL: Link
Lajili K. Embedding Human Capital into Governance Design: A Conceptual Framework. Journal of Management & Governance, 2015, vol. 19, iss. 4, pp. 741–762. URL: Link
Ibarra C.A. Investment, Asset Market, and the Relative Unit Labor Cost in Mexico. Economic Change and Restructuring, 2016, vol. 49, iss. 4, pp. 339–364. URL: Link
Haykin S. Neironnye seti: polnyi kurs [Neural Networks and Learning Machines]. Moscow, Vil'yams Publ., 2006, 1104 p.
Baestaens D.-E., van den Berg W.-M., Wood D. Neironnye seti i finansovye rynki: prinyatie reshenii v torgovykh operatsiyakh [Neural Network Solutions for Trading in Financial Markets]. Moscow, TVP Publ., 1997, 236 p.
Kruglov V.V., Borisov V.V. Iskusstvennye neironnye seti. Teoriya i praktika [Artificial neural networks. Theory and practice]. Moscow, Goryachaya liniya – Telekom Publ., 2002, 382 p.
Osovskii S. Neironnye seti dlya obrabotki informatsii [Neural networks for information processing]. Moscow, Finansy i Statistika Publ., 2002, 344 p.
Deboeck G.J., Kohonen T. Analiz finansovykh dannykh s pomoshch'yu samoorganizuyushchikhsya kart [Visual Explorations in Finance: With Self-Organizing Maps (Springer Finance)]. Moscow, AL'PINA Publ., 2001, 317 p.
Ignat'eva E.D., Mariev O.S. [The methodology and tools of structural-functional analysis of regional development]. Ekonomika regiona = Economy of Region, 2013, no. 1, pp. 227–239. (In Russ.)
Rastunkov V.S., Petrov A.K., Panov V.A. Neironnye seti. Statistica Neural Networks: Metodologiya i tekhnologiya sovremennogo analiza dannykh [Neural networks. STATISTICA Neural Networks: The methodology and technology of modern data analysis]. Moscow, Goryachaya liniya – Telekom Publ., 2008, 392 p.
Kohonen Т. Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 1982, vol. 43, iss. 1, pp. 59–69. URL: Link
Kohonen T. The Self-Organizing Map. Proceedings of the Institute of Electrical and Electronics Engineers, 1990, vol. 78, no. 9, pp. 1464–1480. URL: Link
Kohonen Т., Oja E., Simula O., Visa A.J.E., Kangas J. Engineering Applications of the Self-Organizing Map. Proceedings of Institute of Electrical and Electronics Engineers, 1996, vol. 84, iss. 10, pp. 1358–1384. URL: Link
Rende S., Donduran M. Neighborhoods in Development: Human Development Index and Self-Organizing Maps. Social Indicators Research, 2013, vol. 110, iss. 2, pp. 721–734. URL: Link
Carboni O.A., Russu P. Assessing Regional Wellbeing in Italy: An Application of Malmquist–DEA and Self-Organizing Map Neural Clustering. Social Indicators Research, 2015, vol. 122, iss. 3, pp. 677–700. URL: Link
Martinetz T.M., Berkovich S.G., Schulten K.J. “Neural-Gas” Network for Vector Quantization and Its Application to Time-Series Prediction. IEEE Transactions on Neural Networks, 1993, vol. 4, iss. 4, pp. 558–569.
