ECONOMETRICAL MODELING OF THE STRUCTURE OF MULTIDIMENSIONAL STATISTICAL INTERRELATIONS
DOI:
https://doi.org/10.22478/ufpb.2179-7137.2019v8n6.49197Palavras-chave:
econometrics, correlation analysis, multivariate sampling, statistical model, relationship structureResumo
In economics, the general theory is largely descriptive, and mathematical models are not only statistical but also partial. Therefore, an economic phenomenon usually requires using partial methods and getting only private solutions limited by particular conditions - the type of activity, its place and time of implementation. The real idea of the nature of the economic phenomenon that interests us is given only by statistical data. Correlation analysis is a time-consuming and completely non-formalizable task when it is necessary to justify the relationship structure of a large number of factors. In addition, the quality and interpretation of the results of statistical analysis are predetermined by the nature of the statistical models used to obtain sample estimates of their parameters. Due to the complexity of multidimensional statistical models, general theoretical concepts are usually limited by the assumption that the sampled data does not contradict the normal multidimensional distribution law. This greatly simplifies multivariate statistical analysis and therefore it always leads to linear regression relationships, which corresponds to a trivial system of correlation relationships and is rarely observed in reality. The structure of each economic object is unique, therefore, it is proposed to refine it using a system of correlation matrices of various orders. It is shown that the generalization of large volumes of multidimensional sample data in the form of “portraits” of correlation matrices clearly represents the specific features of the object of study. Moreover, the empirical system of statistically significant relationships is transformed into the corresponding model of economic relationships. Prerequisites are being created for the practical use of universal systems analysis methods based on modern theoretical and software tools of information technologiesDownloads
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Referências
H. Ahrens, J. Leiter, Multivariate analysis of variance. Trans. from German, M.: Finance and Statistics, 1985, 230 p.
A. Afifi, S. Eisen, Statistical analysis: a computer-based approach. Trans. from English - M.: Mir, 1982. - 488 p.
I.N. Drohobytskii. System analysis in economics, UNITI-DANA, 2012, 423 p.
R.L. Ackoff, The art of problem solving, John Wiley & Sons, 1978.
K. Nakamatsu, G.Phillips-Wren, C. Jain Lakhmi, R. Howlett, New Advances in Intelligent Decision Technologies. Results of the First KES International Symposium IDT. Springer, (2009).
Process management / Ed. Becker J., Vilkova L. et al.; [trans. from German]. - M.: Eksmo, 2007. - 384 p.
V.V. Repin, V.G. Eliferov. Process approach to management. Modeling of business processes, Moscow: “Standards and Quality”, 2005, 408 p.
Econometrics: a textbook / I.I. Eliseeva, S.V. Kurysheva, T.V. Kosteev, et al.; Ed. I.I. Eliseeva. - 2nd ed., revised. - M.: Finance and Statistics, 2007. - 576 p. ISBN 978-5-279-02786-6
Econometrics: a study guide / D.F. Fedorov, A.K. Rosenzweig, A.N. Karamyshev, I.F. Nazmiev. - Naberezhnye Chelny: Publishing House of the Naberezhnye Chelny Institute of KFU, 2016. - 104 p.
T. T. Soong, Fundamentals of probability and statistics for engineers, John Wiley & Sons, 2004.
Ch. Dougerty, Introduction to econometrics, Oxford University Press, 1992.
W.H. Greene, Econometric Analysis, 7th ed. Prentice Hall, 2011.
B. Bolch, K.J. Huan, Multidimensional statistical methods for economics. Trans. from English, Moscow: Statistika, 1979, 317 p.
S. Pissanetzky, Sparse matrix technology, London Academic Press, 1984.
George, J. Liu, Computer solution of large sparse positive defines systems, Prentice-Hall, 1981
A. Afifi, S. Eisen, Statistical analysis: a computer-based approach. Trans. from English - M.: Mir, 1982. - 488 p.
I.N. Drohobytskii. System analysis in economics, UNITI-DANA, 2012, 423 p.
R.L. Ackoff, The art of problem solving, John Wiley & Sons, 1978.
K. Nakamatsu, G.Phillips-Wren, C. Jain Lakhmi, R. Howlett, New Advances in Intelligent Decision Technologies. Results of the First KES International Symposium IDT. Springer, (2009).
Process management / Ed. Becker J., Vilkova L. et al.; [trans. from German]. - M.: Eksmo, 2007. - 384 p.
V.V. Repin, V.G. Eliferov. Process approach to management. Modeling of business processes, Moscow: “Standards and Quality”, 2005, 408 p.
Econometrics: a textbook / I.I. Eliseeva, S.V. Kurysheva, T.V. Kosteev, et al.; Ed. I.I. Eliseeva. - 2nd ed., revised. - M.: Finance and Statistics, 2007. - 576 p. ISBN 978-5-279-02786-6
Econometrics: a study guide / D.F. Fedorov, A.K. Rosenzweig, A.N. Karamyshev, I.F. Nazmiev. - Naberezhnye Chelny: Publishing House of the Naberezhnye Chelny Institute of KFU, 2016. - 104 p.
T. T. Soong, Fundamentals of probability and statistics for engineers, John Wiley & Sons, 2004.
Ch. Dougerty, Introduction to econometrics, Oxford University Press, 1992.
W.H. Greene, Econometric Analysis, 7th ed. Prentice Hall, 2011.
B. Bolch, K.J. Huan, Multidimensional statistical methods for economics. Trans. from English, Moscow: Statistika, 1979, 317 p.
S. Pissanetzky, Sparse matrix technology, London Academic Press, 1984.
George, J. Liu, Computer solution of large sparse positive defines systems, Prentice-Hall, 1981
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Publicado
2019-11-27
Como Citar
K. ROZENTSVAIG, A. .; G. ISAVNIN, A. .; N. KARAMYSHEV, A. . ECONOMETRICAL MODELING OF THE STRUCTURE OF MULTIDIMENSIONAL STATISTICAL INTERRELATIONS. Gênero & Direito, [S. l.], v. 8, n. 6, 2019. DOI: 10.22478/ufpb.2179-7137.2019v8n6.49197. Disponível em: https://periodicos.ufpb.br/ojs/index.php/ged/article/view/49197. Acesso em: 19 nov. 2024.
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