Economic Analysis: Theory and Practice
 

Mathematical modeling of a cyclical trend in the investment and construction sphere of the Russian Federation under innovative transformations

Vol. 16, Iss. 11, NOVEMBER 2017

Received: 22 June 2017

Received in revised form: 24 August 2017

Accepted: 23 October 2017

Available online: 29 November 2017

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: С32, С53, О33

Pages: 2177–2188

https://doi.org/10.24891/ea.16.11.2177

Geras'kina I.N. Saint-Petersburg State University of Architecture and Civil Engineering, St. Petersburg, Russian Federation Geraskina82@mail.ru

Kudryavtsev A.Yu. AO All-Russian Scientific Research Institute of Radio Engineering, Moscow, Russian Federation kudral@inbox.ru

Importance At the present stage of its development, the economic science requires timely and qualitative forecasting of economic system's crisis. The article considers new approaches to studying the trends in and management of complex systems, taking into account the natural behavior and properties identified in the process of economic and mathematical modeling.
Objectives The purpose of the study is to devise a mathematical model describing the cyclical development of the Russian investment and construction sector for qualitative forecasts and support to management decisions.
Methods We apply the analysis of the phase space to identify the existing attractors of the economic system, transition between them, and conditions facilitating a switch from one state to another. The phase curve provides a visual representation of the trend in economic system's development, which is essential for making strategic decisions based on economic-mathematical modeling.
Results We have developed a user-friendly economic and mathematical model. It enables to use statistical data and describe cyclic and transition processes; to qualitatively predict the value of a new cycle of the Russian investment and construction sector; to identify the sensitivity of parameters of the order to the dynamics of governing variables, the bifurcation state and behavior of the object under certain managerial influence.
Conclusions Approximation of statistical data, analysis of phase curves of resulting variables helped obtain a mathematical model of cyclic development of the economic system under consideration.

Keywords: investment-construction sector, economic system, modeling, cyclical development

References:

  1. Petrov A.A., Geras'kina I.N. [Analysis of functioning and development of rf investment-construction complex]. Vestnik MGSU, 2016, no. 12, pp. 131–144. (In Russ.)
  2. Asaul A.N., Zaguskin N.N. et al. Samoorganizatsiya, samorazvitie i samoregulirovanie sub"ektov predprinimatel'skoi deyatel'nosti v stroitel'stve [Self-organization, self-development and self-regulation of business entities in construction]. St. Petersburg, IPEV Publ., 2013, 320 p.
  3. Telichenko V.I. [The status and problems of sustainable development of construction activities]. Vestnik MGSU, 2015, no. 12, pp. 5–12. (In Russ.)
  4. Akaev A.A., Sarygulov A.I., Sokolov V.N. Strukturnaya dinamika sovremennykh ekonomicheskikh sistem [Structural dynamics of modern economic systems]. St. Petersburg, Polytechnic University Publ., 2014, 170 p.
  5. Yas'kova N.Yu. [Logic of factorial analysis under the conditions of variative development environment (methodological aspect)]. Vestnik MGSU, 2016, no. 3, pp. 144–151. (In Russ.)
  6. Myasnikov A.A. Sinergeticheskie effekty v sovremennoi ekonomike: vvedenie v problematiku [Synergy effects in modern economy: Introduction to the problems]. Moscow, LENAND Publ., 2011, 160 p.
  7. Malinetskii G.G. Matematicheskie osnovy sinergetiki: khaos, struktury, vychislitel'nyi eksperiment [Mathematical foundations of synergetics: Chaos, structures, computational experiment]. Moscow, LKI Publ., 2007, 312 p.
  8. Akaev A.A., Rumyantseva S.Yu., Sarygulov A.I., Sokolov V.N. Strukturno-tsiklicheskie protsessy ekonomicheskoi dinamiki [Structural and cyclic processes of economic dynamics]. St. Petersburg, Polytechnic University Publ., 2016, 392 p.
  9. Kolesnikov A.A. Sinergeticheskie metody upravleniya slozhnymi sistemami: teoriya sistemnogo sinteza [Synergetic methods of complicated systems control: The system synthesis theory]. Moscow, URSS Publ., 2012, 240 p.
  10. Knyazeva E.N., Kurdyumov S.P. Osnovaniya sinergetiki. Rezhimy s obostreniem, samoorganizatsiya, tempomiry [Bases of synergetics. Modes with aggravation, self-organization, the world of events that is single for various objects]. Moscow, Aleteiya Publ., 2002, 217 p.
  11. Haken H. Sinergetika [Erfolgsgeheimnisse der Natur]. Moscow, URSS Publ., 2015, 414 p.
  12. Ivanter V.V. [A strategy of transition to economic growth]. Problemy prognozirovaniya = Problems of Forecasting, 2016, no. 1, pp. 1–7. (In Russ.)
  13. Zatonskii A.V. [Advantages of differential models in ecological-economic modeling]. Izvestiya Tomskogo politekhnicheskogo universiteta = Bulletin of Tomsk Polytechnic University, 2012, no. 5, pp. 134–139. (In Russ.)
  14. Kovalevskii D.V. [Modelling the 'global economy – global climate' system using the optimization and system-dynamic approaches]. Nauchno-tekhnicheskie vedomosti SPbGPU. Ekonomicheskie nauki = Saint-Petersburg State Polytechnic University Journal. Economics, 2016, no. 6, pp. 197–203. (In Russ.)
  15. Yanchenko T.V., Zatonskii A.V. [Regional social potential model based on second order regression-differential equation]. Novyi universitet. Ser.: Tekhnicheskie nauki = New University. Technical Sciences, 2014, no. 5-6, pp. 23–34. (In Russ.)
  16. Lebedev V.I., Lebedeva I.V. Matematicheskie modeli sinergeticheskoi ekonomiki: monografiya [Mathematical models of synergistic economy]. Stavropol, North Caucasus State Technical University Publ., 2011, 232 p.
  17. Potapenko V.V. [Results of the 24th International Conference on Inter-industry Modeling INFORUM]. Problemy prognozirovaniya = Problems of Forecasting, 2016, no. 6, pp. 129–130. (In Russ.)
  18. Ringle Ch.M., Sarstedt M., Schlittgen R., Taylor C.R. PLS path modeling and evolutionary segmentation. Journal of Business Research, 2013, vol. 66, iss. 9, pp. 1318–1324. URL: https://doi.org/10.1016/j.jbusres.2012.02.031
  19. Dijkstra T.K. PLS' Janus Face – Response to Professor Rigdon's ‘Rethinking Partial Least Squares Modeling: in Praise of Simple Methods’. Long Range Planning, 2014, vol. 47, iss. 3, pp. 146–153. URL: https://doi.org/10.1016/j.lrp.2014.02.004
  20. Castro I., Roldán J.L. A mediation model between dimensions of social capital. International Business Review, 2013, vol. 22, iss. 6, pp. 1034–1050. URL: https://doi.org/10.1016/j.ibusrev.2013.02.004
  21. Cepeda G., Martelo S., Barroso C., Ortega J. Integrating Organizational Capabilities to Increase Customer Value: A Triple Interaction Effect. In: H. Abdi et al. (eds), New Perspectives in Partial Least Squares and Related Methods, Springer Proceedings in Mathematics & Statistics, 2013, vol. 56, pp. 283–293. URL: https://doi.org/10.1007/978-1-4614-8283-3_20

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