The economic determinants of world disorder events: An empirical analysis

Abstract

This paper delves into the economic determinants of World Disorder Events (WDE) using robust empirical methodologies applied to a cross-section of world data over the period 1997-2022. By employing econometric models such as Robust Least Squares (RLS), complemented with Artificial Intelligence methods, such as Ridge Regression (RR) and Artificial Neural Networks (ANN), we uncover the influence of various economic, environmental, and health factors on the frequency and intensity of WDE. RR is used to address multicollinearity among predictors, thereby enhancing the reliability of our regression estimates. The robustness of our findings is further ensured through ANN models, which improve prediction accuracy and model stability. This analysis not only identifies key economic drivers of WDE but also offers policy recommendations to reduce the incidence and impact of these events. By integrating multiple applied techniques, this paper contributes to a more nuanced understanding of the complex interplay between economic conditions and global stability, offering valuable insights for policymakers and economic analysts.