Selected economic time series analysis using the fuzzy linear regression

Abstract

The adequacy of mathematical models of economic systems is reduced by the complexity of their structures, the number of parameters and infl uencing factors. The mathematical regression model assumes that the structure and functional depen dence of the input and output variables of the modeled system is precisely defi ned. However, real systems are complex and indeterminate, and their adequate models must formalize their vague phenomenon. Artifi cial intelligence methods use fuzzy set mathematics and fuzzy logic approaches to synthesize models of indeterminate sys tems. We provided our research of defi ned fuzzy linear regression models using data series of economic variables, namely the evolution of the discount rate, infl ation rate and the rate of unemployment between 2019 and 2021. These data series were cho sen with regard to the selected economic cycle before, during and after the Covid-19 pandemy. It is precisely due to the cyclical development of the economy that some level of uncertainty and vagueness of data of monitored variables is manifested. Re sults of the work refl ect outputs of the proposed fuzzy regression model of indetermi nate variables during the selected time series. These confi rmed the assumptions of the authors that there is a mutual interdependence between the selected economic variables, in particular the amount of the discount rate in relation to the infl ation rate, the amount of the infl ation rate in relation to the rate of unemployment and thus the amount of discount rate in relation to the rate. The existence of time lags in deciding on economic policy measures and their subsequent implementation was also confi rmed in all cases, even during the analyzed time series of three years. Only variable un employment behaved less standardly, as its essence in many respects lies outside of purely pure market mechanism and is under the infl uence of market inelasticity, legal measures, free movement of labor in the EU, etc.