Il'yasov R.Kh.Chechen State University (CHSU), Grozny, Chechen Republic, Russian Federation ilyasov_95@mail.ru https://orcid.org/0000-0001-7040-798X
Subject The article deals with searching for correlation between threads in continuous- time modeling. Objectives The aim is to analyze the efficiency of spline approximation of discrete changes in stock, its transformation into continuous flows with a search for ‘latent’ correlations between economic indicators. Methods If in discrete-time models it is possible to transfer to threads through calculating the chain growth, in continuous-time models it is offered to convert stock into flows through differentiation of the function of stock movements. Results The first derivatives of variables, i.e. threads, enable to find a correlation in the slowdown and acceleration of processes – between the flow of oil exports and the money supply in the Russian economy, which was not observed in the dynamics of absolute indicators of economic movements. Conclusions Spline simulation of changes in money supply enables to convert it into a thread, and then compare it with the flow of oil exports. Between the flows of money supply and oil exports, the ‘hidden’ correlation has now been clearly revealed. It had never been achieved by methods of classical econometrics. In practice, the proposed method of finding correlations could be useful for researching the links between flows, both in the international economy and in individual countries, industries and enterprises. Analytical representation of relationship between stock-to-thread indicators can also be useful for managing the logistics processes.
Il'yasov R.Kh. [Spline-modeling and analysis of interactions in the economy with the possible presence of switching points of regression]. Nauchno-tekhnicheskie vedomosti SpbGPU. Ekonomicheskie nauki = SPbPU Journal. Economics, 2018, vol. 11, no. 4, pp. 165–175. (In Russ.) URL: Link
Tubol'tsev M.F. [Using of invariants in financial flow modeling]. Nauchnye vedomosti Belgorodskogo gosudarstvennogo universiteta. Ser.: Ekonomika. Informatika, 2009, no. 7, pp. 146–151. URL: Link (In Russ.)
Strel'tsova E.D., Bogomyagkova I.V., Strel'tsov V.S. [Linguistic approach to budget flow modeling]. Nauchnye vedomosti Belgorodskogo gosudarstvennogo universiteta. Ser.: Ekonomika. Informatika, 2012, no. 1, pp. 156–161. URL: Link (In Russ.)
Melikhov M.B., Borisov A.N. [Methodological peculiarities of modeling of function of financial and credit sphere]. Izvestiya Tul'skogo gosudarstvennogo universiteta.Ekonomicheskie i yuridicheskie nauki = Izvestiya Tula State University. Economic and Legal Sciences, 2016, iss. 4, part 1, pp. 99–106. URL: Link (In Russ.)
Baturina E.V., Litvinenko A.N. [Monitoring of Shadow Cash Flows Using Computer Modelling]. Ekonomika regiona = Economy of Region, 2018, vol. 14, no. 1, pp. 326–338. URL: Link (In Russ.)
Lipovina-Božović M., Ivanović M. Capital Flows in Montenegro: SVAR Model. Zbornik Radova Ekonomskog Fakulteta u Rijeci, 2018, vol. 36, iss. 2, pp. 647–675. URL: Link
Splain-funktsii v ekonomiko-statisticheskikh issledovaniyakh: sbornik statei [Spline functions in economic and statistical research: A collection of articles]. Novosibirsk, Nauka Publ., 1987, 206 p.
Botasheva F.B., Vintizenko I.G. [“New Econometrics” with Its “Thin” Methods Researches of Economic Conjunctures]. European Social Science Journal, 2014, no. 10-1, pp. 31–39. (In Russ.)
Cáceres-Hernández J.J., Martín-Rodríguez G. Evolving Splines and Seasonal Unit Roots in Weekly Agricultural Prices. Australian Journal of Agricultural and Resource Economics, 2017, vol. 61, iss. 2, pp. 304–323. URL: Link
Vintizenko I.G., Il'yasov R.Kh. Novaya ekonometrika: monografiya [New econometrics: a monograph]. Stavropol, Agrus Publ., 2018, 480 p.
Kaishev V.K. Lévy Processes Induced by Dirichlet (B‐)Splines: Modeling Multivariate Asset Price Dynamics. Mathematical Finance, 2013, vol. 23, iss. 2, pp. 217–247. URL: Link
Odoardi I., Muratore F. Understanding the Support of Savings to Income: A Multivariate Adaptive Regression Splines Analysis. Advances in Intelligent Systems and Computing, 2015, vol. 373, pp. 385–392. URL: Link
Vygodchikova I.Yu., Gusyatnikov V.N., Vysochanskaya E.Yu. [Method of spline-approximation of economic dynamics with unsustainable trend]. Vestnik Saratovskogo gosudarstvennogo sotsial'no-ekonomicheskogo universiteta = Vestnik of Saratov State Socio-Economic University, 2017, no. 4, pp. 110–115. URL: Link (In Russ.)
Cleophas T.J. Clinical trials: Spline modeling is wonderful for nonlinear effects. American Journal of Therapeutics, 2016, vol. 23, no. 3, pp. e844–e849. URL: Link
Bessonov V.A. Vvedenie v analiz rossiiskoi makroekonomicheskoi dinamiki perekhodnogo perioda [Introduction to the analysis of the Russian macroeconomic dynamics of the transition period]. Moscow, CEMI RAS Publ., 2003, 151 p.
Vintizenko I.G., Yakovenko V.S. Ekonomicheskaya tsiklomatika: monografiya [Economic Cyclomatic: a monograph]. Moscow, Finansy i statistika, Stavropol, Agrus Publ., 2008, 428 p.
Floc'h F.L, Oosterlee C.W. Model-Free Stochastic Collocation for an Arbitrage-Free Implied Volatility, Part II. Risks, 2019, vol. 7, iss. 1, pp. 1–21. URL: Link
Campos E.L., Cysne R.P. A Time-Varying Fiscal Reaction Function for Brazil. Estudos Econômicos, 2019, vol. 49, iss. 1, pp. 5–38. URL: Link
Kim Y.-J. Efficient Estimation and Variable Selection for Partially Linear Single-Index-Coefficient Regression Models. Communications for Statistical Applications and Methods, 2019, vol. 26, iss. 1, pp. 69–78. URL: Link
Luchko M., Lew G., Ruska R., Vovk I. Modelling the Optimal Size of Investment Portfolio in a Non-State Pension Fund. Journal of International Studies, 2019, vol. 12, iss. 1, pp. 239–252. URL: Link
