RUDN Journal of EconomicsRUDN Journal of Economics2313-23292408-8986Peoples’ Friendship University of Russia2130510.22363/2313-2329-2019-27-1-113-121Research ArticleThe specificities of the research in the economics’ development of SevastopolPiskunElena I.<p>Doctor of Economics, Associate Professor, Рrofessor of the Department of Finance and Credit</p>lenapiskun@mail.ruKhokhlovVladimir V.<p>Cand. Sci. (Tech.), Assistant Professor of the Department of Finance and Credit</p>khokhlov_vv57@mail.ruSevastopol State University1512201927111312119062019Copyright © 2019, Piskun E.I., Khokhlov V.V.2019<p>A comprehensive study of regional processes implies a qualitative analysis of indicators over time, which is necessary not only to identify current trends, but also to make forecasts that are used in the development of regional development strategies and programs. In order to study the development of the city of Sevastopol on the basis of statistical data on the state of the economy in the Ukrainian and Russian periods, as well as determine the possibility of their use for making forecasts, it is necessary to solve the issue of homogeneity of the series of economic indicators. The existing criteria for verifying the homogeneity of data are not applicable to the solution of the issue of compatibility of multidimensional series belonging to different time intervals. The article proposes the use of exploratory factor analysis to solve this problem. However, the lack of statistical data leads to a degeneration of the matrix of pairwise correlations of economic indicators. To obtain estimates of the parameters of the factor model, a generalized inverse matrix is used, which is obtained as a result of a matrix iterative procedure. Exploratory factor models for the Ukrainian and Russian periods of Sevastopol have fundamental differences, and the corresponding multidimensional series cannot be combined for a holistic study of economic processes in the region.</p>city economyregionSevastopoldevelopmentmultidimensional time seriesexploratory factor modelэкономика городарегионСевастопольразвитиемногомерные временные рядыэксплораторная факторная модель[Argyros I.K. (2008). Convergence and Applications of Newton-type Iterations. Springer Science + Business Media, LLC. doi: 10.1007/978-0-387-72743-12][Courtney M.G.R. (2013). Determining the number of factors to retain in EFA: Using the SPSS R-Menu v2.0 to make more judicious estimations. Practical Assessment, Research and Evaluation, 18(8), 1-14.][Decell Jr. H.P., Kuhng S.W. (1966). An Iterative Method for Computing the Generalized Inverse of a Matrix. NASA Technical Note, NASA - ITN - D-3 464, 16.][Fabrigar L.R., Wegener D.T., MacCallum R.C., Strahan E.J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, (4), 272-299.][Kantorovich L.V. (1945). Ob odnom effektivnom metode reshenija ekstremal’nyh zadach dlja kvadratichnogo funkcionala [On one effective method for solving extremal problems for a quadratic functional]. Doklad akademii nauk SSSR, 48, 485-487. (In Russ.)][Khokhlov V.V. (2018). Klasterizacija regionov metodami eksploratornogo faktornogo analiza [Clusterization of regions by the methods of exploratory factor analysis]. Ekonomika i upravlenie: teorija i praktika, 4(4(1)), 87-94. (In Russ.)][Lankaster P. (1982). Teorija matric [Theory of matrices]. Moscow: Nauka Publ., 272. (In Russ.)][Ruscio J., Roche B. (2012). Determining the number of factors to retain in an exploratory factor analysis using comparison data of a known factorial structure. Psychological Assessment, 24(2), 282-292. doi: 10.1037/a0025697][Spada N. (2017). Form-Focussed Instruction and SLA: a review of classroom and laboratory research. Language Teaching, 30(2), 73-87.][Tabachnick B.G., Fidell L.S. (2015). Principal components and factor analysis. Using multivariate statistics (4th ed). Needham Heights, MA: Allyn & Bacon, 582-633.]