Application of Spatial Econometric Analysis to Assess the Convergence of Budget Revenues of Russian Regions
Purpose of the study. Within the framework of this study, attention is focused on the assessment of convergence using the budget revenues of the regions of Russia per capita as a key index. Currently, research in the field of economic growth and macroeconomics remains in the center of attention of t...
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| Format: | Article |
| Language: | Russian |
| Published: |
Plekhanov Russian University of Economics
2025-01-01
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| Series: | Статистика и экономика |
| Subjects: | |
| Online Access: | https://statecon.rea.ru/jour/article/view/1861 |
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| Summary: | Purpose of the study. Within the framework of this study, attention is focused on the assessment of convergence using the budget revenues of the regions of Russia per capita as a key index. Currently, research in the field of economic growth and macroeconomics remains in the center of attention of the scientific community and government agencies, as they play a key role in shaping the development strategies of regions and countries as a whole. In this context, one of the essential aspects is the assessment of convergence, that is, the process of convergence of economic indexes between different regions.Materials and methods. The study examines key methods and models of spatial econometric analysis, such as unconditional convergence, global Moran indexes, etc. Much attention is paid to the interpretation and formation of economically sound conclusions based on the results of econometric modeling.Results. This article is devoted to the application of spatial econometric analysis in the context of the study of differentiation of economic development of Russian regions. Spatial regression analysis is a tool for studying the relationships between various economic variables in various geographical areas, while considering various dependencies and spatial autocorrelation.Conclusion. The results of the study have empirical significance for political and economic decision-making at the regional and national levels. The article provides an important contribution to the methodology of econometric analysis in macroeconomics, expanding the understanding of the relationships between economic variables in various geographical areas. |
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| ISSN: | 2500-3925 |