AI-Driven Structural Modeling for Predicting Macroeconomic Shocks
DOI:
https://doi.org/10.64137/31079423/IJEBMR-V2I2P102Keywords:
AI-Assisted Macroeconomics, Structural Modeling, Macroeconomic Shocks, Machine Learning, DSGE Models, Structural VAR, Nonlinear Economic Dynamics, Deep Learning, Economic Forecasting, Policy Simulation, Regime Shifts, Economic StabilityAbstract
Macroeconomic shocks such as financial crises, supply-chain disruptions, geopolitical events, and abrupt policy changes pose significant challenges to conventional forecasting models due to their nonlinear dynamics, structural breaks, and high-dimensional data environments. This paper proposes a hybrid AI-driven structural modeling framework that integrates the theoretical rigor of dynamic structural models with the predictive power of modern machine learning techniques. By embedding economic theory into AI architectures, the framework enhances shock identification, captures nonlinear transmission mechanisms, and improves early-warning capabilities for both anticipated and unanticipated macroeconomic disturbances. Using a combination of structural VARs, DSGE-based constraints, deep neural estimation, and reinforcement learning for policy simulation, the study demonstrates how AI can estimate latent structural parameters, detect regime changes, and model complex interactions across monetary, financial, and real-economy variables. Simulation experiments and empirical applications using global macroeconomic datasets indicate that AI-enhanced structural models significantly outperform traditional benchmarks in both accuracy and speed of shock prediction. The research contributes to the growing intersection of AI and macroeconomics by providing a systematic methodology for theoretical consistency, interpretability, and data-driven predictive power, offering new tools for policymakers, central banks, and financial institutions.
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