The purpose of the study. Establishing the specifics of reproduction of the Russian population in the modern conditions of economic development in general and in the context of federal districts.

Materials and methods. In the process of writing the article, the authors used regulatory legal acts of the Government of the Russian Federation, materials of the Federal State Statistics Service, the works of individual scientists and public organizations on the problems of population reproduction. In the course of the work, such statistical research methods as tabular and graphical methods, analysis of indexes of dynamics series were used.

Results. The population of the Russian Federation and the components of its change for 1990-2021, the dynamics of fertility and mortality rates for 1950-2021 were studied, the reasons for the decline in the total fertility rate and the net coefficient of population reproduction, including in the context of federal districts, were considered.

Conclusion. Currently, the population of the Russian Federation has not reached the level of 1990. Fluctuations in the total population growth are mainly caused by fluctuations in migration growth. In conditions of low fertility and high mortality, the migration component is the determining factor of population growth. Since 1992, the natural decline of the population has been established (with the exception of 2013-2015). The amount of migration growth exceeded the natural decline in 2009-2017, which ensured the overall population growth.

The intersection of the curves of the dynamics of the fertility rate and the mortality rate in the Russian Federation was established in 1992 and 2016. From 2012 to 2016 the coefficient of natural growth is approaching zero, and then a downward trend is formed, that is, a decrease in the population.

The main reason for the decline in the birth rate in Russia is the demographic, socio-economic state of society, in which a steady decline in the total fertility rate, demographic aging of the population, and changes in the family structure have been established. Over the past 30 years, there has been a narrowed reproduction of the population in the country, which can be considered as a potential depopulation.

A high variation of the total fertility rate in the context of federal districts of Russia has been established. In the Central Federal District and the North-Western Federal Districts for the period 1990-2021, the total fertility rate did not exceed the average Russian level of this index. Over the past three years (2019-2021), the total fertility rate has been decreasing in the Northwestern, Southern, Volga, and Siberian Federal Districts.

In order to eliminate negative trends in the birth rate in the regions, it is necessary to create jobs, form normal housing conditions, ensure sustainable growth in real income of the population, organize a modern level of healthcare, education, develop a network of preschool institutions, and support families.

.Assessing the situation in the labor market of the region, and ultimately forecasting the level of employment and unemployment, is a complex multidimensional  mathematical  problem  that  does not have a reliable and proven solution to date. Research in the direction of creating evaluation methods and forecasting the situation on the labor market is an actual task. The subject of the work is an assessment of the situation on the labor market of the Volga Federal District, through the analysis and modeling of the coefficient of tension in the labor market. The purpose of the study was to develop a methodology that allows making a preliminary forecast of the situation on the labor market of the subject of the Volga Federal District. The information base of the study was the results of a sample survey of the labor force conducted by state statistics bodies in accordance with the classifier of objects of administrative-territorial division (OKATO – Russian Classification on Objects of  Administrative  Division).  To  date, the length of the time series is four time periods (2018–2021) of the year. As methods of analysis and modeling, methods of descriptive statistics were used, as well as mathematical modeling of the relationship between the coefficient of tension in the labor market and the rating index of the socio-economic situation of the Volga Federal District. It is shown that the statistical grouping of the coefficient of tension in the  labor  market  as  a  whole  for  all  subjects  of the Russian Federation is extremely heterogeneous, which makes it difficult to model processes in the labor market. At the same time, the statistical grouping of the coefficient of tension in the labor market of the regions of the Volga Federal District is quite homogeneous, which makes it possible to carry out simulations using such an integral index as the rating of the socio-economic situation of the subject of the Volga Federal District. In the process of modeling in the environment of the SPSS computer program, a nonlinear regression model of the relationship between the coefficient of tension in the labor market (dependent variable) and the place in the rating of the socio-economic situation of the subject of the Volga Federal District (explanatory variable) was formed. The model allows you to make a preliminary forecast of the situation on the labor market of the subject of the Volga Federal District.

Purpose of the study. The article analyzes the impact of world oil prices (external and internal factors) on the country’s GDP, considers fluctuations in world oil prices, their impact on the national economy of Azerbaijan and the integrability of these macroeconomic indexes.

Materials and methods. The study of the dynamics of the functioning of time series based on the initial data revealed their non-stationarity, which does not allow creating a “qualitative” predictive model. In order to achieve the goals of the study and “improve the quality” of the model being formed, which is used to calculate predictive estimates, appropriate econometric procedures were carried out and the integrability of time series was investigated. In particular, the method of vector error correction model VECM is used. The test is based on the use of cointegration equations between variables, where lag lengths and Granger causality definitions are solved within this model. When forming the VECM model, the hypotheses put forward in the work were tested using econometric tests. The responses of the impulse function to the independent variables of the model were studied by the method of graphical representation based on the values of the model and its residuals.

Results. It has been determined that the long-term equilibrium relationship between variables can be considered stable, since after short-term disturbances from shock reactions, stability is restored. The applied method of decomposition of forecast error variances to determine the influence of exogenous variables on the endogenous variable showed that the greatest uncertainty in the forecast for GDP, Azeri_light, Brent and West is given by their own changes during the first trimester of the period under consideration.

Conclusion. The results obtained can be useful for identifying real trends in Azerbaijan’s GDP and determining its interdependencies with other macroeconomic variables, for determining its interdependencies with variations in energy prices based on an analysis of the dynamics of the indexes under consideration, for developing recommendations and forming directions for the longterm development of GDP.

The relevance of the study is explained by the link between economic growth and investment in human capital.

The purpose of the study is a comparative analysis of the dynamics of the return of physical and human capital in the European and Asian regions of Russia.

Materials and methods. In the article, the authors use the extended Cobb-Douglas production function and conduct a regression spatial analysis based on data of the official Russian statistics.

Results. There are estimates of the relationship between the income of the population of regions with the number of employed, with the volume of fixed assets of regions and human capital; the wage fund of the population of regions with the number of employed, with the volume of fixed assets of regions and human capital, the elasticity coefficients are justified. The authors analyzed and interpreted the values of the elasticity coefficients. In particular, the coefficients of elasticity of household income by fixed assets for Asian and European regions tend to decrease; the authors explain this by the low level of investment in the renewal of fixed capital at enterprises. The coefficients of elasticity of income of the population by the number of people employed in the economy of the regions are noticeably higher and tend to increase. At the same time, human capital has a stronger impact on the income of the population than fixed assets and the number of people employed in the economy of the regions. There is a decrease in the influence of human capital on the income of the population in the group of European regions of Russia at the end of the period under review and the relative stability of its returns in the Asian group of regions.

Conclusion. The impact of human capital on the income of the population differs significantly in the European and Asian regions of Russia. The authors identified efficiency trends and features of the use of the main factors of production in different groups of regions of the Russian Federation in the period under review. Scientific significance is the generalization of knowledge about the role of human capital in economic development, technology transfer and increasing the investment attractiveness of settlements and regions due to it; obtaining statistically significant models for assessing the economic return of human capital.

The purpose of the study. To characterize the level and dynamics of the country’s economic development, its sovereignty and, accordingly, the adoption of effective management decisions, quantitative information is needed on economic assets, in particular financial ones, their presentation in national accounts, monetary statistics and indexes of the external sector.

The aim of the paper is to systematize theoretical and practical developments on the definition and presentation of data on financial assets in modern statistics of Russia’s foreign economic relations, to identify areas for improving methods for assessing and analysis of financial assets, a system of indexes characterizing their presence, structure and dynamics.

Materials and methods. In the paper, the authors considered the definitions, specifics, classifications and categories of financial assets and liabilities, applied structural and dynamic  data  analysis,  as well as methods of theoretical research in the form of generalization, comparison and special analytical procedures based on official statistics from Rosstat, the Bank of Russia, the Ministry of Finance and international statistical organizations.

Results. The paper identifies the main directions for the statistical

study of financial assets, based on current international standards, taking into account national practice. Topical issues of theory and practice of observations and presentation of data on financial assets in foreign economic statistics, application of definitions and classifications of international standards to Russian official statistical activities are considered. The features of the data presentation on financial assets in macrostatistics, in the system of indexes of statistics of foreign economic relations, are formulated. The authors conducted a study of the structure and dynamics of indexes of Russia’s foreign economic statistics that characterize the state and movement of financial assets and liabilities representing them.

Conclusion. Statistical analysis of foreign economic transactions with financial assets allow us to identify not only the main trends in the development of these processes, but also makes it possible to analyze the relationship between the subsectors of the Financial corporations’ sector, between this sector and other sectors, as well as a comprehensive change in the volume and composition of stocks and flows of financial assets as a result of the exchange between residents and non-residents.

The authors comprehensively reviewed the methodological framework for constructing the financial account of the Balance of payments, International investment position, including in the context of financial instruments. The Balance of payments and International investment position, through their system of statistical indexes, reflect international economic relations and represent an important tool for the study of financial assets. The groupings of items of the financial account of the Balance of payments are based not only on the categories of financial instruments, but also on the functional categories of investments and classifications of institutional sectors, which serve the purposes of a comprehensive reflection of financial assets in foreign economic relations. Thus, it is obvious that the main task of such a presentation is to reflect financial assets in terms of the type of instruments, as well as to analyze the role of a particular sector in the implementation of relevant transactions.

In the International investment position, financial assets are also fully reflected in that part of them that is involved in economic transactions between residents and non-residents. Groups of financial assets and liabilities are given in a classification similar to that used in the Balance of payments: by functional purpose, by financial instruments, by institutional sectors, by maturity. Together with the financial account, account figures for other changes in financial assets and liabilities explain the total amount of changes: increases or decreases in the value of financial assets / liabilities, their occurrence or disposal, resulting from transactions, revaluation and other changes in volume.

The issues of theory and practice of presenting indexes of financial assets in the considered sections of macrostatistics and their analysis are a relevant and promising direction for improving national statistical accounting, incl. due to the spread of remote work, the collection of information via the Internet, the emergence of a large number of new financial instruments and operations.

The authors of the article have repeatedly referred to the problems of macroeconomic financial statistics in their studies, which gave them the opportunity to comprehensively consider the topical issues of representing financial assets in the statistics of foreign economic relations.

The purpose of the study is to analyze the potential of statistical modeling in predicting the prices of the Bitcoin cryptocurrency and its impact on the economy. In the course of the article, answers were received to such questions as: What is the impact of macroeconomic events on the dynamics of the Bitcoin price? How quickly does the cryptocurrency market stabilize after the falls? How effective is statistical modeling to solve the problem of predicting the price of Bitcoin? Which model shows the best results? What measures of regulation and control of the cryptocurrency market are necessary at the stage of its formation in the Russian Federation?

Materials and methods. Historical data on average monthly Bitcoin closing prices and macroeconomic events such as the COVID-19 pandemic and the Russian-Ukrainian conflict were collected and analyzed. The paper uses statistical models, including ARIMA and LSTM, to predict future Bitcoin prices based on historical data. The accuracy of the models was calculated based on such indexes as the mean absolute error (MAE) and the mean square error (MSE).

Results. Analysis of the impact of macroeconomic events showed that during the crisis, the attractiveness of Bitcoin increased and investors used this asset as a new investment tool. During the analysis of the consequences of the Russian-Ukrainian conflict for the cryptocurrency market, its reaction to geopolitical events was revealed according to the increased liquidity indexes in the market. In the process of modeling the dynamics of the average monthly Bitcoin price, the model with parameters (1, 1, 0) at MAE = 15.03% was recognized as the best ARIMA model. The LSTM neural network model on a similar data set showed a MAE error equal to 2.57%.

Conclusion. The analysis shows that itcoin was the most attractive investment tool during the crisis, which led to a sharp increase in its price in 2021. The Russian-Ukrainian conflict has also affected its price, causing a significant decline in 2022. However, statistical modeling methods predict an increase in the price of Bitcoin in the first half of 2023, and governments may consider regulating or controlling its use to reduce risks associated with the cryptocurrency market. The recommended measures are the introduction of regulations, the introduction of transaction taxes, the development of national digital currencies, public education and the prevention of criminal activity.