Macroeconomic Crisis Early Warning Model for Libya Using Machine Learning

Abstract

Macroeconomic instability in Libya is closely linked to oil-sector volatility, fiscal fragmentation, exchange-rate pressure, liquidity shortages, and interruptions in public financial management. These conditions make delayed policy responses costly and create a need for a transparent early warning model that can translate macroeconomic signals into timely risk alerts. This study develops a machine learning-based early warning model for predicting macroeconomic crisis risks in Libya using secondary macroeconomic indicators and a scenario-based validation design. The proposed framework integrates data cleaning, lag construction, volatility measurement, crisis-risk labelling, model comparison, and interpretable risk explanation. The model architecture compares conventional statistical classification with tree-based ensembles, support vector learning, and neural network approaches. The results show that nonlinear ensemble methods are conceptually more suitable for Libya because they can capture interactions among oil disruption, fiscal pressure, exchange-rate pressure, liquidity stress, and inflation acceleration. The proposed risk dashboard classifies macroeconomic conditions into low, moderate, and high-risk states and links each alert to the main contributing indicators. The discussion highlights that the model should not replace expert judgement, but it can strengthen evidence-based policy monitoring, improve institutional coordination, and provide earlier signals for fiscal, monetary, and reserve-management decisions. The study concludes that an interpretable early warning system can support Libya’s macroeconomic resilience if regularly updated with reliable official data and governed through transparent validation procedures.