The destructive consequences of the COVID-19 pandemic have negatively affected socioeconomic indicators and disrupted macroeconomic stability. The aim of the study is to determine the optimal combination of financial, socioeconomic, and public health determinants based on their relevance for the post-pandemic recovery of macroeconomic stability. For this purpose, principal component analysis was used to form an initial macroeconomic stability index by integrating such indicators as GDP growth, unemployment rate, consumer price index, current account balance, and trade volume. Next, the Granger test and panel data regression modeling was employed to identify the causality between the level of macroeconomic stability and a set of financial, socioeconomic and public health determinants. Finally, the financial, socioeconomic, and public health determinants were ranked according to their impact on macroeconomic stability. The obtained empirical results can be used to improve the financial, economic, and health care state policies in terms of strengthening country resistance to risks caused by a pandemic or other similar threats in the future.

The unfolding of the COVID-19 pandemic has revealed "bottlenecks" not only in the healthcare system, which was unable to cope with a significant influx of patients and quickly eliminate the spread of coronavirus infection, but also the vulnerability of the socioeconomic systems of countries all over the world. The research aims to determine country-specific trend patterns of volatility of the integral level of macroeconomic stability and its components and forecast their values for the medium term to determine the dynamics of post-pandemic recovery. The implementation of the research objectives involves the implementation of the following steps: 1) determining outliers in data series that characterise the dynamics of the components of macroeconomic stability in the context of each of the 10 studied countries; 2) eliminating outliers; 3) determining the highest-quality functional form of the dependence of the change in the corresponding individual macroeconomic indicator over time; 4) forecasting the change in individual indicators and the integral level of macroeconomic stability for the medium term (2023-2025); 5) determining the deviations of the forecast values of the above indicators from their pre-pandemic level (2019) and end-of-pandemic level (2022); 6) qualitative interpretation of the forecasting results.

The pandemic of coronavirus disease (COVID-19) significantly slowed economic development and exacerbated income inequality. However, the scale of this destructive influence varies considerably among countries. Thus, the purpose of this study is to identify changes in causality patterns between economic development and income inequality due to COVID-19. To fulfill the task, an Index of Economic Development (IED) is developed using the multivariate analysis tools, Cronbach's alpha and the Fishburn formula. The Gini index was chosen as a core proxy of social inequality. The research covers a sample of 15 European countries. The period of the analysis is 2000-2021. A set of regression equations are constructed to determine the relationship between economic development and income inequality in the studied countries. A dummy variable is integrated into the equation to determine the impact of the coronavirus disease (COVID-19). Modeling is carried out on the panel data. The most acceptable functional form of the regression model is clarified based on the Hausman test. Modeling results make it possible to identify patterns of changes in the impact of income inequality on economic development, and vice versa. The change in their causality due to COVID-19 is substantiated.