Метод решения обратных задач экономического анализа на основе статистических данных

Abstract

The inverse problem answers the question of "how do I...?" and the purpose of solving such a problem is the ability to form optimal management decisions. This article presents the methods for solving inverse problems of economic anal-ysis using statistical data. In a classic case using inverse computations, calculation of the increments of the arguments is based on the target value of the function, the coefficients of the relative importance of the arguments, the primary values of the arguments and the directions of their change. This proposed method involves determining the functional argu-ments based on statistical historical data and includes two steps: first, constructing the regression equation from among the argument, the resulting indicator and the definition of the value of the arguments and then finding the solution to the inverse problem using the inverse computations. The coefficients of relative importance are calculated based on the magnitude of the gradient, and the sign of the elemental gradient determines the direction in which the arguments change. This paper describes the solution to the simple problem of forming revenue for the organization. The article presents an example of the application of this method to solve the modeling problem of rating the Republic of Tuva, which is based on eight groups of indicators: the standard of living, financial security, agricultural production efficiency, construction efficiency, the availability of labor resources, health, security education, and the technology for information and communication security. Using this method has allowed us to answer the question of how it is possible to increase the integral characteristics of the regional socio-economic development by 4.68%. This study used the inverse calculation theory, regression analysis, and optimization methodology. This method, which we have developed, can be used in the decision-making support systems for management.

Keywords

• inverse problem
• inverse computations
• statistical data
• the regression function
• economic analysis