articles

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.