Bohdan Kovalov

The paper deals with analysis of the mechanism of macroeconomic imbalance estimation and achieving the convergence of national economy. With this purpose the authors summarized the main approaches to define the macroeconomic imbalance. In addition, the main indicators which influence macroeconomic imbalance are allocated. On the basis of obtained results, the authors offer to employ the macroeconomic imbalance procedure which is used in EU countries for investigation. In order to achieve this external, internal and employment indicators in EU were analyzed by authors. Besides, with the purpose to indicate Ukrainian place comparing with EU, in particular with Visegrad Countries, the main indicators of MIP for Ukraine were calculated by the authors. According to the results, the authors made conclusion that the Ukrainian economy can be characterized as not stable (as in Bulgaria, Hungary and the Slovak Republic). Moreover, the authors allocated for the future research the necessity to understand the power of countries impact to each other with the purpose to achieve and save the convergence of national economy.

Over the years, technological progress has accelerated highly, and the speed, flexibility, human error reduction, and the ability to manage the process in real time have become more critical and required production companies to adapt production and business models according to the needs. The demand for real-time decision support systems adapted to these raising business needs is continuously growing. Nevertheless, businesses usually face challenges in identifying new indicators, data sources, and appropriate financial modeling methods to analyze them. This paper aims to define and summarize the main financial/economic forecasting methods for production companies in the context of Industry 4.0. Main findings show forecasting accuracy of up to 96% when combining economic and demand information, optimal forecasting period from 10 months to five years, more frequent use of soft indicators in forecasting, the relationship between company’s size and production planning. Four groups of indicators used in financial modeling, such as (I) production-related, (II) customers’ and demand-oriented, (III) industry-specific, and (IV) media information indicators, were separated. The analysis forms a suggestion for decision-makers to pay more attention to the forecasting object identification, indicators’ selection peculiarities, data collection possibilities, and the choice of appropriate methods of financial modeling.

Acknowledgment
This work was partly supported by Project No. 0121U100470 “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy”.

This study aims to identify whether achieving sustainable development goals influences SMEs’ development and assess its degree. The dataset on SMEs’ development indicators and SDGs 2, 8, 9, 12, and 13 for the panel of EU-27 countries in 2011–2020 was collected using Eurostat and OECD datasets. Breusch and Pagan Lagrangian multiplier test for pooled OLS/panel data random effects and Hausman test for fixed/random effects were utilized. The results were in favor of random effect GLS regression for SDG2 models, SDG9 models, and SDG12-13 (Model 1) and fixed effect GLS regression for SDG8 models and SDG12-13 (Model 2), respectively. Based on bibliometric analyses using VOSViewer 14 and a comprehensive literature review, 19 independent variables have been selected from the “Sustainable development indicators” catalog covering five sustainable development goals; SMEs’ turnover and SMEs’ employees employed are used as the dependent variables to reflect SMEs’ development. The empirical evidence suggests a significant relationship between individual sustainable development and SMEs’ development indicators. It was found that all seven sustainable development indicators of SDG 2 (Zero hunger) and SDG 12 (Responsible consumption and production) have a significant relationship with the indicators of SMEs’ development. Instead, only a part (8 out of 13) of the sustainable development indicators of SDG 8 (Decent work and economic growth), SDG 9 (Industry, innovation and infrastructure), and SDG 13 (Climate action) have a significant relationship with two or one of the SMEs’ development indicators. Therefore, achieving sustainability goals stimulates the development of SMEs itself.

Acknowledgment
This study is supported by the British Academy’s Researchers at Risk Fellowships Program (Award Reference: RaR\100673).