Abstract
The issue raised in the article is the study of the dependence of export and import of agricultural machinery on its production in Russia. The Russian agricultural machinery market is influenced by three groups of factors: 1) own production of agricultural machinery; 2) export of agricultural machinery of Russian production; 3) import of agricultural machinery of foreign production. Traditionally, such problems are solved using multivariate analysis. However, in this case, the use of this method is problematic for a number of reasons: the source data is dimensional and measured in different units, the number of observations is less than the number of factors, the factors depend on each other, and the number of factors is too large. These restrictions are proposed to be overcome by applying automated system-cognitive analysis and its software tools of the intellectual system “Eidos”. For this purpose the following tasks were solved: 1) formulation of the idea and the concept of problem solving; 2) justification of the choice of the method and tool for solving the problem; 3) application of the selected method and tool to solve the problem; 4) evaluation of the effectiveness of the proposed solution of the problem; 5) consideration of the restrictions and disadvantages of the proposed solution of the problem and the prospects for its development by overcoming these restrictions and disadvantages. Some results of solving these problems are briefly summarized in this article.
Highlights
The problem solved in the article is the study of the dependence of export and import of agricultural machinery on its production in Russia
The market of agricultural machinery in Russia is influenced by three groups of factors: 1) Own production of agricultural machinery in Russia; 2) Export of agricultural machinery of Russian production (19 factors); 3) Import of agricultural machinery of foreign production (24 factors)
Factor analysis imposes strict requirements on the source data, which in practice is very difficult to provide. In this case, the application of this method is problematic for a number of reasons: the initial data are dimensional and measured in different units: own production of agricultural machinery in Russia is measured in physical terms by type of equipment, and export and import are measured in terms of value; the number of observations (8 years) is less than the number of factors; all these factors depend on each other; the number of factors is generally too large; all combinations of factor values should be presented in the initial data; the data should be absolutely accurate, etc
Summary
The object of modeling is described by 51 factors Such problems are solved by statistical methods, in particular, multivariate analysis. Factor analysis imposes strict requirements on the source data, which in practice is very difficult to provide In this case, the application of this method is problematic for a number of reasons: the initial data are dimensional and measured in different units: own production of agricultural machinery in Russia is measured in physical terms by type of equipment, and export and import are measured in terms of value; the number of observations (8 years) is less than the number of factors; all these factors depend on each other; the number of factors is generally too large; all combinations
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