Subject. The article analyzes the mesodynamics of economic sectors in Russian regions. Objectives. The aim of the study is to undertake quantitative and qualitative monitoring of the components' evolution in twelve basic economic sectors from 2005 to 2017, to describe the regions' homogeneity, stability and balance, and their prospects for investment. Methods. We employ methods of identification, lowess smoothing, and the bootstrap approach to identify models of mesodynamics on 30–50-value samples, without a priori knowledge about the stochastic component distribution law. Results. The findings confirmed the possibility and adequacy of the econophysics paradigm for mesodynamic analysis. We justified and tested methods for general mesodynamic models' identification, using the R programming language. Conclusions. The offered methodology and tools will enable to develop a knowledge base for fundamental laws of mesodynamics. They will also help reveal spatial and temporal features of cyclical development of Russian regions; assess the level of balance and sustainability of regional development in the medium term period; group the regions by their cycle length and define the most promising ones for investment.

Subject. The article provides the results of meso-dynamics analysis of main twelve industries, based on monthly data for 82 Russian regions, from January 2005 till December 2020. Objectives. The purpose of the study is to address the problem of balanced and stable spatial development of Russia’s regions and Russia, which requires modeling of adequate tools and forecasting nonlinear mesodynamics. Methods. The study follows the econophysics methodology. Results. We consider additive and multiplicative interactions of regular time series components between each other and the residuals, thus expanding the scope of tools application for the variety of considered industries and their models. Using the common and new trend models, we analyze structural changes, introduce the topological measure of proximity to the neighborhood of residuals with heavy-tailed distribution, which is estimated by median values of trends and cycles for regular components. The traditional time series decomposition (by trend, cycle, seasonality, and residual) is supplemented by our unique complex of wavelet transformations, which forms the models of cycles, using auto regression. We obtained representative and time-synchronized analytical estimates of regular components of industries’ dynamics for meso- and macro-indicators of the Russian economy that have higher accuracy than the known results for the accuracy of modeling and forecasting. Conclusions. The offered methodology and tools enable a more adequate analysis of non-linear dynamics of regions’ middle-term development. They help shift to growth point identification, create the atlas of economic industrial cycles, analyze stages of bifurcations and scenario predictive planning.

The differentiation in the development of regions remains a major challenge for the working out-of-state industrial and regional policies aimed at balanced and sustainable development. In theory, regional differences between internal and border regions can be explained by differences in natural resources, and economic and industrial potential, as well as by the existence of external boundaries. Border regions have higher risks in ensuring the geo-political sustainability of an industry. External boundaries, as well as differences in industry dynamics between regions, cycle stages, and industry trends, are often overlooked in industrial policy making, which in itself can be a factor of volatility. In this research based upon the Russian economy, we test the hypothesis that it is possible to define the industrial cycle with the help of the index of production. The analysis is based on the official Russian statistics from January 2005 to December 2021. To test the hypothesis, an original 12-step method of analysis was used, which allows such a mathematical model to be selected that will best describe the industry cycle and allows the trend to be estimated. The cyclic dynamics were assessed with the help of structural and parametric identification of modeling and the forecasting of trajectories of evolving dynamics based upon econophysics methodology, the use of median trends, and wavelet analysis. The comparative study was made based on the example of four sectors: the food, chemical, pharmaceutical (production of medicines and materials used for medical purposes), and automotive industries. The results show, first, that there are significant differences in the dynamics of industry cycles in both the internal and the border regions, which need to be taken into account to implement the progressive economic structure and specialization strategies of a region. Secondly, the group of border regions in the food, chemical, and pharmaceutical industries is growing at a higher rate.

Subject. This article deals with the problems of development of promising sectors of the region's economy. Objectives. The article aims to assess the scenarios of regional industry changes considering a case study of the automotive industry. Methods. For the study, we used the bootstrap and wavelet transform techniques. Results. The article describes the inertial, pessimistic (5% percentile) and optimistic (95% percentile) scenarios for the development of the automotive industry and shows the range of possible deviations of industry dynamics in the case of the influence of external and internal factors. Conclusions. The proposed methodology and tools make it possible to fine-tune the forecast of industry dynamics, take into account its possible deviations from a predetermined path during the year, and put management decisions into proactive impact effect.

ПОЛИТИКА ИННОВАЦИОННОГО ИМПОРТОЗАМЕЩЕНИЯ В УСЛОВИЯХ ЦИКЛИЧНОСТИ РЕГИОНАЛЬНОЙ ЭКОНОМИКИ