The use of structural vector autoregression to assess the mutual impact of consumption and average wage levels

At present, it is an urgent task to describe macroeconomic phenomena through the construction of qualitative mathematical models. In the current economic landscape, the interaction among economic factors is of significant importance. Therefore, it is necessary to understand the relationships between key indicators that significantly contribute to economic growth. In particular, the interactions between these indicators can be investigated using vector autoregressive models, in which they are treated as endogenous variables. The strength of the relationship between variables can be determined through the use of impulse response functions, which provide an accurate economic estimation only when a vector autoregressive model is converted into a structural vector autoregressive model. In this study, we selected several indicators as objects of research, including the volume of food and non-food retail sales and the average wage level in the Volga Federal District of Russia. An econometric model was constructed using vector autoregression to analyze the relationships between these variables, and the model was verified and tested for its forecasting ability, confirming its high quality. Impulse response functions were also used to assess the mutual influence of these indicators, which were derived from the vector autoregression model after it was converted into a structural vector autoregression model. Finally, we analyzed the mathematical findings and provided them with economic interpretation.