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Abstract.
The dependence of the emergent characteristic-returns to scale, calculated on the basis of production functions in the regions of the Russian Federation, on various indicators characterizing the regions, including macroeconomic indicators, characteristics of economic reproduction processes and socio-economic institutions, geographical indicators, etc. was investigated. The calculations were carried out using the method of optimal valid partitioning, which implies verification of patterns in the data using permutation tests. As a result of the analysis, a statistically significant relationship with returns to scale was revealed for a significant percentage of indicators or their combinations, which indicates both the real existence of the interdependence of these indicators with the returns to scale, and the sufficient accuracy of estimates of the parameters of the Cobb-Douglas model for the time series involved in the analysis.
Keywords:
data mining, optimal valid partitioning, pattern verification, permutation tests, production functions, returns to scale, regional economics, economic reproduction.
PP. 20-31.
DOI 10.14357/20718632210103 References
1. Cobb, C.W., and P.H. Douglas. 1928. A Theory of Pro-duction. American Economic Review, Supplement, Papers and Proceedings of the Fortieth Annual Meeting of the American Economic Association 18(1):139–165. 2. Chubrik, A.S. 2002. Otdacha ot masshtaba proizvodstvennoi funktsii i obshchefaktornaya proizvoditel'nost': primer Pol'shi i Belorussii [Returns to scale of production function and general factor productivity: the example of Poland and Belarus]. EKOVEST 2(2):252–275. 3. Gafarova, E.A. 2013. Modelirovanie regional'nogo razvitiya na osnove proizvodstvennykh funktsii [Modeling of regional development based on production functions]. Internet-zhurnal "Naukovedenie" [Internet-journal “Science of science”] (3):1–7. https://naukovedenie.ru/PDF/39evn313.pdf. 4. Kirilyuk, I.L. 2013. Modeli proizvodstvennykh funktsii dlya rossiiskoi ekonomiki [Models of production functions for the Russian economy]. Komp'yuternye issledovaniya i modelirovanie. [Computer Research Modeling] 5(2): 293–312. 5. Kirilyuk, I., and O. Senko. 2019. Verification of the Returns to Scale of Production Type for the Russian Federation Regions. EPJ Web of Conferences. 224(06011):1-6. 6. Regiony Rossii. Sotsial'no-ekonomicheskie pokazateli [Regions of Russia. Socio-economic indicators] 2014: Stat. sb. / M.: Rosstat, 2014. 7. Regiony Rossii. Sotsial'no-ekonomicheskie pokazateli [Regions of Russia. Socio-economic indicators] 2017: Stat. sb. / M.: Rosstat, 2017. 8. Rossiiskii statisticheskii ezhegodnik [Russian Statistical Yearbook] 2015: Stat.sb./ M.: Rosstat, 2015. 9. Kirilyuk, I.L., and O.V. Sen'ko. 2020. Vybor modelei optimal'noi slozhnosti metodami Monte-Karlo (na primere modelei proizvodstvennykh funktsii regionov Rossiiskoi Federatsii) [Assessing the validity of clustering of panel data by Monte Carlo methods (using as example the data of the Russian regional economy)]. Informatika i ee primeneniya. [Informatics and Applications] 14(2):111-118. 10. Grebnev, M.I. 2015. Postroenie proizvodstvennykh funktsii regionov Rossii [Construction of production functions of Russian regions]. VUZ. XXI vek [High School. XXI Century] 2:50–56. 11. Sen'ko, O.V. 2003. Perestanovochnyi test v metode optimal'nykh razbienii [Permutation test in the optimal partitioning method]. Zhurnal vychislitel'noi matematiki i matematicheskoi fiziki [Computational Mathematics and Mathematical Physics] 43(9):1438-1447. 12. Kirilyuk, I.L., Kuznetsova, A.V., Sen'ko, O.V., and A.M. Morozov. 2017. Method for detecting significant patterns in panel data analysis. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications) 27(1):94-104. 13. Borisova, L.R., Kuznetsova, A.V., Sergeeva, N.V., and O.V. Sen'ko. 2020. Primenenie metodov mashinnogo obucheniya dlya sravneniya kompanii Arkticheskoi zony RF po ekonomicheskim kriteriyam v sootvetstvii s reitingom Polyarnogo indeksa [Application of machine learning methods for comparing companies in the Arctic zone of the Russian Federation by economic criteria in accordance with the Polar Index rating]. Komp'yuternye issledovaniya i modelirovanie [Computer Research Modeling] 12(1):201-215. 14. Pesarin F., and L. Salmaso. 2010. Permutation Tests for Complex Data. Chichester, UK: Wiley. 15. Senko, O.V., and A.V. Kuznetsova. 2010. A recognition method based on collective decision making using systems of regularities of various types. Pattern Recognition and Image Analysis 20(2):152–162. 16. Kirdina, S.G., and S.Yu. Malkov. 2010. Dva mekhanizma samoorganizatsii ekonomiki: model'naya i empiricheskaya verifikatsiya (nauchnyi doklad) [Two mechanisms of self-organization of the economy: model and empirical verification (scientific report)]. M.: Institut ekonomiki RAN, 69 s. 17. Maevskii, V.I., and S.Yu. Malkov. 2014. Perspektivy makroekonomicheskoi teorii vosproizvodstva [Perspectives of the macroeconomic Reproduction Theory]. Voprosy ekonomiki [Economic issues] 4:137-155. 18. Dixit, A.K., and J.E. Stiglitz. 1977. Monopolistic Competition and Optimum Product Diversity. The American Economic Review 67(3):297–308. 19. Krugman, P. 1991. Increasing Returns and Economic Geography. Journal of Political Economy 99(3):483-499.
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