The globalization process and the war in Ukraine show us that migration is one of the strongest global trends in the modern economy. For this paper, we determined three types of migration, depending on the intention of the people involved, these being labor, educational, and refugee migration. Each type has a different influence on the macroeconomic process. However, in this paper, we investigate the influence of general migration on GDP. We analyze five factors that have major influences on GDP, namely, migration (I), interest rate (IR), active population (AP), export (E), and the consumer price index (CPI). For the purposes of this paper, vector autoregressive models (VAR models) were chosen to perform the analysis. We used the Granger causality test to investigate the lag structure and identified the exogenous variables in the VAR model, such as GDP, migration, and the active population. We investigated the cross-influence between these factors and found that migration has a negative effect on the active population and a positive effect on GDP, while GDP growth leads to a decrease in migration. The Akaike and Schwartz criteria showed the high quality of the VAR models. The impulse analysis of shock influences identifies the structure of the reaction seen in GDP and migration, depending on their shock factors. Using decomposition analysis, we found that migration and GDP influence each other by 10–14%, which can improve the forecasting of these factors and the study of structural migration by the use of these three types.

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This article is devoted to the analysis of the impact of socio-economic shocks on the dynamics of higher education development. It is substantiated that, on the one hand, higher education influences the development of society and the economy, on the other hand, the development trends of a country provide both opportunities and limitations for its development. An algorithmic model for studying the impact of social and economic shocks on the development of the higher education system (HES) has been developed. To diagnose the relationship between higher education and the socio-economic development of Ukraine and Slovakia, the following indicators were used: GDP per capita, the Human Development Index, school enrollment, tertiary, and net migration. The presence of nonlinear trends in the change in indicators has been shown and portraits of the socio-economic development of the countries have been constructed. To assess the impact of socio-economic shocks on the HES, the time-series decomposition method and cross-spectral analysis were used. The time-series decomposition allowed us to identify cyclical components of indicators, based on applying cross-spectral analysis, and the most significant local harmonics and the lag of their influence on the occurrence of shocks in the HES were determined. The use of the developed models allows us to predict periods of shock points in the HES depending on shocks in the tendencies of GDP per capita and net migration.

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In the current work we investigation depend of Ukrainian financial markets segments from influences of the external financial market. In the article we propose the methodology of the investigation which includes three main units. The main ideas of these units are recognition of the most influential external financial market by indicators set, forecasting of the tendencies and influences of the foreign financial markets segments, construction of adaptation decision for the regulation of the Ukrainian financial market. We used the VAR models and variance analysis for the determination of the influences foreign financial market. The investigation of MosPrime Index and DAX Index as most influential indicators of external market allowed to define the adaptation type of the stock and credit segments of the Ukrainian financial market.

The purpose of the study is to provide a comparative analysis of approaches to analyzing the trends in cryptocurrency dynamics. The paper analyzes the trends in cryptocurrency development, which has shown an increase in the influence of cryptocurrency on the structure of the financial market. It has been determined that from 2013 to 2023, the capitalization of cryptocurrency market increased almost 1 000 times. However, in recent years, the number of "fake" cryptocurrencies has also increased, so the total number of cryptocurrencies has almost not changed in the last two years. The works of researchers on the analysis of the trends in cryptocurrency exchange rates have been studied, and three main approaches to the analysis have been formed. The main components of the first approach have been investigated, the influence of miners, mining costs, blockchains, and the interaction of mining participants on the formation of exchange rates has been determined. It is found that the necessity to analyze the cryptocurrency market in conjunction with other elements of the financial market is the key aspect of the second approach to the study of trends in the exchange rate of cryptocurrencies. In this approach, cryptocurrency acts as an alternative to centralized components of the financial market and as an element of financial freedom. The study of the works of the third approach allows to identify the main methods and models for analyzing the dynamics of exchange rates, among which the main place is occupied by: models of time series analysis taking into account sentiments (Sentiment-Enriched Time Series Forecasting – SETS models), deep learning models for forecasting of processes with long and short-term memory, recurrent neural networks, and gated recurrent unit models.