Unveiling Global Economic Stratification: A Machine Learning Framework for Multi-Dimensional Macroeconomic Analysis

Introduction: Traditional econometric approaches to multi-country macroeconomic analysis face critical limitations in capturing complex, non-linear relationships across diverse economic systems. Objective: This study aims to introduce a comprehensive machine learning framework, implemented in Python, that transcends conventional VAR model constraints by analyzing 13 key macroeconomic indicators across 217 countries (2010–2025). Method: Advanced clustering techniques (K-means) and ensemble learning (Random Forest), along with Principal Component Analysis (PCA), were applied to reveal hidden economic stratification patterns previously undetectable through traditional methods. Result: The analysis uncovers four distinct global economic clusters representing differentiated development trajectories, with middle-income economies comprising the majority of observations (57.4%). Fiscal indicators demonstrate exceptional forecasting accuracy through Random Forest algorithms, while growth dynamics remain inherently unpredictable, revealing fundamental asymmetries in economic system behaviors. Conclusions: This study demonstrates that machine learning techniques, implemented in Python, can systematically identify which macroeconomic relationships are structurally determined versus stochastically driven. This differential predictability framework provides immediate policy implications for targeted intervention strategies, enabling policymakers to focus resources on controllable fiscal mechanisms rather than pursuing futile attempts to predict volatile growth patterns.