ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and low‐frequency (e.g., quarterly/annual) data availability. This study proposes a novel hybrid framework integrating dynamic stochastic general equilibrium (DSGE) modeling with ML techniques to address these limitations, focusing on the evolution of France's public debt. We first generate a large artificial macroeconomic dataset using an estimated DSGE model for France, which allows for efficient training of ML algorithms. These trained models are then applied to actual historical data for directional debt forecasting. The results show that the best machine learning model is an XGBoost achieving 90% accuracy, outperforming an elastic net model, used as benchmark. Our results highlight the viability of combining structural economic models with data‐driven techniques to improve macroeconomic forecasting.
Sofianos et al. (Thu,) studied this question.