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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 technologies

ECONOMETRICAL MODELING OF THE STRUCTURE OF MULTIDIMENSIONAL STATISTICAL INTERRELATIONS