SOCIO-ECONOMIC CYCLES: THEORETICAL VISION AND STATISTICAL ASSESSMENTS

Abstract

This article examines the theoretical foundations and statistical assessments of socio-economic cycles, emphasizing their interdisciplinary nature and historical significance. The research explores how cyclical patterns manifest in various aspects of civilization, including economic, political, and social processes. The authors analyze classical and modern theories of cyclical development, referencing thinkers such as Heraclitus, Polybius, Machiavelli, Vico, Kondratiev, and Schumpeter. The study highlights the significance of statistical methods in identifying economic cycles, using global mesoeconomic indicators such as inflation rates, unemployment, and GDP trends over the past 25. A key focus is placed on the role of interdisciplinary research, combining philosophy, economics, history, and statistics to provide a comprehensive analysis of cyclical patterns. The article discusses the evolution of cyclical theories from ancient philosophical reflections to modern economic models. The statistical component of the study involves the use of smoothing methods, statistical equations of dependences, and complex statistical coefficients to determine trends in the global economy. The findings suggest that short-term (7-year) economic cycles can be observed, reflecting the broader rhythms of socio-economic development. The study also underscores the influence of monetary policies, such as the Federal Reserve’s interest rate decisions, on global economic stability. The article concludes that understanding socio-economic cycles is crucial for anticipating crises and economic trends, emphasizing the importance of statistical tools in economic forecasting.

This research contributes to the broader discourse on cyclical development, offering valuable insights for economic historians, statisticians, and policymakers. It calls for further interdisciplinary investigations to refine theoretical models and enhance predictive capabilities in socio-economic research.