Economy of the Neighborhood Belt Countries in the Context of the COVID-19 Pandemic and Peculiarities of Post-Crisis Recovery

Aim. The presented study aims to identify key factors affecting the macroeconomic situation in the EAEU countries and Tajikistan (hereinafter referred to as the region) in the context of the COVID-19 pandemic.Tasks. This study summarizes trends in the development of the neighborhood belt countries in recent years; investigates changes in the real, monetary, fiscal, and external sectors of the economies caused by the spread of COVID-19; identifies the vulnerability factors of the countries in the region and key trends in responding to the crisis and post-crisis recovery.Methods. To investigate the effects of the COVID-19 pandemic on the macroeconomic situation in the neighborhood belt countries, the authors analyze the dynamics of major macroeconomic indicators characterizing the state of the real, monetary, external, and fiscal sectors of the economies under consideration.Results. The macroeconomic situation in the neighborhood belt countries at the beginning of the COVID-19 pandemic and during the post-crisis recovery is largely similar. The effects of the pandemic include sharp depreciation of national currencies, increased budget deficits, and increased national debt. Post-crisis recovery in the region is characterized by persistent risks to fiscal and debt sustainability and the effect of pro-inflationary factors.Conclusions. Despite the consistent post-crisis recovery of the neighborhood belt countries, sustainable growth is still threatened by the possible deterioration of the epidemiological situation, national budget deficit, high level of national debt, and increasing inflationary pressure. Macroeconomic policy in the countries of the region can be improved by enhancing the macroeconomic forecasting system and applying budget rules for managing budget and debt risks.

Big Data-Driven Macroeconomic Forecasting Model and Psychological Decision Behavior Analysis for Industry 4.0

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis.

Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia

Since July 2021, Banco de la República strengthened its forecasting process and communication instruments, by involving predictive densities on the projections of its models, PATACON and 4GM. This paper presents the main theoretical and empirical elements of the predictive density approach for macroeconomic forecasting. This model-based methodology allows to characterize the balance of risks of the economy, and quantify their effects through a joint probability distribution of forecasts. We estimate this distribution based on the simulation of DSGE models, preserving the general equilibrium relationships and their macroeconomic consistency. We also illustrate the technical criteria used to represent the prospective factors of risk through the probability distributions of shocks.

BUDGET MECHANISM: CHALLENGES OF TODAY

The definition of "budgetary mechanism" was analyzed in the article. It is described as a form by which the fund of financial resources is managed by influencing economic entities. The components of the budgetary mechanism that do not function effectively and lead to cyclical consequences in particular, the deficit of budget funds, tax pressure on taxpayers and, as a consequence, the accumulation of public debt, are identified. The shortcomings of certain components of the budget mechanism are substantiated, in particular: budget planning and forecasting; as analysis showed, are carried out inconsistently with each other. Thus, the State Budget of Ukraine is formed not depending on the capacity of the economy and its subjects, but depending on the needs of passive budget items. Passive budget items are growing systematically. There are a number of remarks on the applied forms of the budgetary mechanism, for example, payment of taxes and customs payments to the budget is ineffective, because a significant share of taxes is in the "shadow". Therefore, the amount of shadow budget funds is 30%, including smuggling 9.3%, payment of salaries in envelopes 5%, offshore schemes 3.6%, conversion centers 1.8%. The reasons for the inefficiency of the budgetary mechanism in terms of legal support are systematic changes in legislation, which are sometimes caused by lobbying motives. Ways to improve the budgetary mechanism are identified, for example, comparison of macroeconomic forecasts and budget planning; involvement of business in the processes of macroeconomic forecasting and budget planning; de-shadowing of the economy, including transparent payment of salaries through the implementation of a progressive taxation; payment of customs duties; State budget discharge through the introduction of compulsory health insurance and additional pension insurance levels.

Scaled PCA: A New Approach to Dimension Reduction

This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example on macroeconomic forecasting shows that the sPCA has better performance in general.