The article presents the study results on the changes in the Russian economy related to effects of the specific circumstances of 2020–2022. The authors support the conclusion about the ongoing shift in the economic development model and make several proposals for domestic economic policy. The authors note that the strong negative influence of external factors can, under certain conditions (on the principle of «challenge-and-response» or «shock-and-reaction»), create momentum towards the accelerated structural transformation of the economy and transition to a new national economic cycle adapted to current trends in scientific and technological development.

The paper analyses indicators of dynamics and structural changes in the final use of gross domestic product (GDP), in production, and in investment activity. The article describes the differences in the development trends for three major sectors of the economy in which its industries were incorporated: the raw materials and processing sector, the infrastructure sector, and the innovation sector, and also for the aggregate «other industries» (tabular material contains detailed analytical data on enlarged categories).

The article presents the results of GDP dynamics factor analysis based on two approaches – using the intersectoral model and the GDP dynamics macroeconomic function. Based on the first approach, the authors obtained the overall estimates of the impact of changes in export volumes and domestic final demand on GDP in 2022, as well as the decrease in the import intensity of production and other estimates. Based on the second approach, the authors obtained the estimate of the potential GDP rate, its gap with the actual rate, and the influence of the main factors on economic dynamics.

The features of the new development model (transition to development based mainly on internal sources of funds, resources, etc.) are indicated. The paper outlines authors' views on the conditions that can meet the challenges of developing the country as part of the new development model. The issues of making the investment forecasts and providing investment process with the financial resources are considered. According to the authors, further development of the systematic approach to managerial decision-making and transition to ensuring better coherence between vital economic development directions makes it possible to achieve economic and social objectives more effectively.

The paper emphasizes the relevance of improving methodological tools for macroeconomic forecasting. In particular, it is pointed out, that models with a large number of explanatory variables on relatively short samples can often overfit in-sample and, thus, forecast poorly. The article reviews studies on forecasting inflation in Russia and explains the applicability of the model with Bayesian shrinkage of time-varying parameters based on hierarchical normal-gamma prior. Models of this type allow for possible nonlinearities in relationships between regressors and inflation and, at the same time, can deal with the problem of overfitting.

The choice of a system of statistical indicators used to forecast monthly inflation in Russia during the period 2011–2022 is substantiated. It is shown that at short forecast horizons (of one to three months) Bayesian normal-gamma shrinkage TVP model with a large set of inflation predictors outperforms in forecasting accuracy, measured by mean absolute and squared errors, its linear counterpart, linear and Bayesian autoregression models without predictors, as well as naive models (based on random walk). At the horizon of six months, the autoregression model with Bayesian shrinkage exhibits the best forecast performance. As the forecast horizon rises (up to one year), statistical differences in the quality of forecasts of competing models of Russian inflation decrease.

The developed method can be used by the Bank of Russia and executive authorities for rapid assessment of inflation forecasts until the end of the year in order to evaluate risks of inflation deviation from the target level and elaborate preventive economic policy measures.

The article discusses the features of applying statistical analysis in marketing during digital transformation. After explaining the relevance of the study, the authors formulate the challenges of using statistical analysis in marketing, reveal the study goals, objectives, and tools, and provide an overview of the content of statistical analysis (at the company level). In modern Internet marketing, a large number of statistical indicators are used, such as CPC (cost per click), CTR (advertisement click-through rate), CPA (advertising cost), conversion rate, lead cost, and many others. However, according to the authors, the indicators used in most companies are not comprehensive enough. The paper notes that statistical analysis of the digital marketing domain uses several digital products, such as Google Analytics, Adobe Analytics, Mixpanel, Salesforce Analytics Cloud, and Looker. The more specific the information required for statistical analysis, the more the company strives to create its digital product for collecting, analyzing, and interpreting the most important data for marketing about hidden patterns in customer behavior, in their consumer journey, motives, and incentives for their choice. The uniqueness of the artificial intelligence algorithms used for this is ensured by the methods of classification, clustering, regression, and association analysis that are used in machine learning and statistical analysis for the automatic processing and analysis of large amounts of data. The article focuses on the specific results of improving marketing activities based on the development of statistical analysis amidst digital transformation arising from the introduction of digital technologies, and the development of digital products that ultimately give competitive advantages in a particular area of business.

The urgency of improving mathematical and statistical tools for analyzing agricultural complex across the territories of our country depends on the gravity of the issue of interregional differentiation of the scale of development of agricultural production, which plays a crucial role in the implementation of the Food Security Doctrine of the Russian Federation.

The introductory part of the article presents an overview of the literature on food security issues, an analysis of the current state of domestic agriculture, and directions for its development. The body of the article explains the research methodology, which allows to classify, first andforemost, agricultural regions (indicating their territorial location, socio-economic characteristics, and predictive characteristics). As an indicator of the intensity of agricultural development in the regions, the authors used the volume of agricultural production per capita, which became a criterion for selecting 25% of the regions with the maximum values of this indicator. That said, by an agro-region (or agricultural region) is meant a region that produces more agricultural production than needed and exports its surplus outside the region or country (food exports). In the multidimensional classification of agro-regions, three clusters are determined (according to the level of socio-economic development, the development efficiency of the economic activity «Agriculture, forestry, hunting, and fishing», and the volume of crop and livestock production). The paper analyses the dynamics and predictive characteristics of the development of the cluster-average volume of agricultural production per capita (including crop production and animal husbandry). An analysis of the dynamics of exports of food products and agricultural raw materials per capita by clusters revealed that the regions of the first cluster are the most actively developing, and the regions of the third cluster (with the most developed animal husbandry) are oriented towards domestic consumers.

According to the authors, during the analyzed period in Russia, there was a positive trend in the development of agriculture, with the development of agro-regions outpacing all-Russian trends. The highest development rates are typical for agro-regions with a balanced development of crop and livestock production (regions of the first cluster).

The study aims at a comprehensive, spatial-temporal assessment, based on regional statistics, of the intensity of development of higher education in the Volga Federal District (VFD) concerning the economic and demographic characteristics of the constituent entities of the VFD.

The paper presents the results of a statistical analysis of key indicators of training of personnel with higher education in the constituent entities of the VFD. It also explains the influence of sectoral characteristics of regional economies on the structure of university graduates by areas of training and specialties. The authors argue that there are significant imbalances and negative trends in the dynamics of the main indicators of training of personnel with higher education in the constituent entities of the VFD. If the current trends continue, by 2024, in the VFD regions the indicators of the number of students in education programs for bachelor, specialist, and master per 10 000 population and the number of graduated bachelors, specialists, and masters per 10 000 population are expected to decrease by 36–58% compared to the level of 2015.

The article emphasizes the features identified based on inter-regional comparative analysis in the system of fields of training and special- ties of higher education in constituent entities of the VFD, due to the transformation in the economic sphere, do not adequately correspond to the existing sectoral structure of the regional economy.

According to the authors, achieving the Sustainable Development Goals by 2030, to a large extent, will depend on a substantial increase in the regional capacity of higher education in each of the constituent entities of the VFD.

As long as the Chinese economy moves to the forefront in the world, it’s becoming increasingly important to improve the methodology for analyzing its development as well as to identify drivers of its growth. The paper provides a statistical analysis of dynamics of the main indicators characterizing the scientific sphere of the country in 2005–2019: number of R&D personnel; gross domestic expenditures on R&D; patent and publication activities of Chinese scientists. In addition, the relationship between the size of the country's GDP and its high-tech exports was analyzed. For the purpose of studying the indicators in their dynamics a regression analysis based on the Chinese official statistical data was carried out.

The paper also presents a forecast of the scale and effectiveness of the China's scientific activity for the period 2022–2024 according to which, while maintaining the existing trends in the development of science and the economy, an increase in the volume of domestic expenditures on R&D is expected in 2024 (more than 3.284 trillion yuan) as well as in the number of issued patents (more than 537 thousand) and in the number of published scientific articles (more than 2.22 million). The carried-out analysis showed the existence of a close relationship between the country's economic growth and the dynamics of its exports of high-tech products. According to the forecast, while maintaining the existing trends in the development of the economy and high-tech exports, the projected values of Chinese GDP are in 2022 – 18.6 trillion US dollars, in 2023 – 20.3 trillion, and in 2024 – 21.7 trillion US dollars.

The results of the study showed that China's rapid economic growth was driven by both large-scale capital investments, high rates of increase in labor productivity, and the successful development of science and innovation in all strategic sectors of the economy. The exponential and parabolic growth of almost all key indicators characterizing personnel and financial components of the research sphere, patent, and publication activities, makes it possible to draw a conclusion on strengthening the leadership in the economic position of the People's Republic of China in the world and increasing its scientific potential.

The paper discusses the author's proposals for addressing the problem of achieving the comparability of Russian socio-economic indicators related to the change in the borders of the state as a result of the special military operation. The experience of ensuring indicators comparability when changing state borders, namely during the unification of Germany in 1990 and reunification with Crimea in 2014, is considered. It is shown that, due to the long duration of the process of changing borders and the uncertainty of its results, the specificity of the discussed Russian episode of changing state borders makes it unique, i. e., having no direct analogues in recent decades for developed countries. The article discusses requirements for data comparability, which are imposed by the tasks for which they are used. The need to find such a solution to the problem, which would allow analyzing the dynamics of indicators for both a comparable and entire territories, is explained. The author proposes a simple and technologically advanced approach to solving the problem of comparability. Features of its solution for indicators of different types, different levels of aggregation, and with different time steps are considered. Recommendations for organizing the work related to ensuring the comparability of statistical indicators when changing state borders are proposed.

The authors present their views on the modern concept of statistics as a science, academic discipline, and field of activity. The urgency of the matter lies in an adequate understanding of the essence of statistics, which determines the direction for its further evolution as a tool for increasing the effectiveness of the national socio-economic processes.

According to the authors, by the beginning of the XXI century, de jure statistics are associated with activities, measured by data, that reflect a set of phenomena of diverse nature while ignoring de facto activities, associated with an assessment of the quantitative side of mass phenomena as part of information support of cognitive processes and management decision-making in the socio-economic sphere. In the former case, theoretical, methodological, and practical statistical activities are based on the consideration of the statistical population as such, for which is being developed an appropriate toolkit based primarily on the unity of methods of mathematical logic, mathematical statistics, and big data analysis. In the latter case, theoretical, methodological, and practical activities are interpreted within the framework of the concept implying that the quantitative side of mass social phenomena is the object of cognition and management as an objective reality. It is based on the following categories: a statistical indicator, a system of indicators, and an information model.

Analysis of the main features of the two types of activity show that their convergence is impossible. The paper concludes that while maintaining the independent status of the first type of activity, called Statistics, it would be appropriate to acknowledge the independent status of the second type of activity, called Socio-economic statistics, as a type of occupation, a specialty and area of training in higher education, a scientific specialty – a branch of science, incorporating it in the professional standard «Statistician», the educational standard of higher education «Socio-economic statistics», and the standard of the scientific specialty «Socio-economic statistics».