Authors

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. Read more...

A generalized approach to assessing the relationships between socio-economic factors based on vector autoregression models

Economic modeling frequently requires assessing the interdependence among multiple socio-economic indicators. These factors typically exhibit a multi-dimensional structure, allowing them to be examined from various perspectives. The aim of this study is to develop a mathematical modeling-based method that enables a comprehensive assessment of macroeconomic processes, identifies significant characteristics of socio-economic entities, and incorporates them into forecasting. The proposed approach relies on estimating impulse response functions and forecast error variance decompositions through vector autoregressive models. A generalized analysis of these inter-factor interaction characteristics provides a holistic understanding of the phenomena under investigation. In the forecasting stage, the varying influence strengths of multi-dimensional factors across different aspects are explicitly accounted for, facilitating the identification of priority measures to enhance regional socio-economic development. The proposed method was empirically tested using a case study on the relationship between social factors of migration flows and employment levels in the Russian Federation. A panel vector autoregression model was selected as the econometric framework, a choice driven by both the research objectives and the structure of the available dataset. The analysis was conducted across three social dimensions characterizing migration flows: the dynamics of working-age population arrivals and departures, education levels, and citizenship status. Using the estimated models, generalized impulse response functions and forecast error variance decompositions were derived. Based on these results, a model ensemble was constructed, a forecast incorporating the relative importance of each variable was generated, and a comparative analysis was performed. Econometric modeling techniques ensure both the scientific rigor and practical relevance of this research. All computations were implemented using the R programming language. Read more...