What question did this study set out to answer?

The aim is to improve public debt forecasting for France using advanced modeling techniques.

March 10, 2026Open Access

Using DSGE and Machine Learning to Forecast Public Debt for France

ESEmmanouil SofianosCentre National de la Recherche Scientifique TBThierry BettiCentre National de la Recherche Scientifique TPThéophilos PapadimitriouDemocritus University of Thrace

Key Points

The aim is to improve public debt forecasting for France using advanced modeling techniques.
Integrated DSGE modeling with machine learning techniques.
Generated a large artificial macroeconomic dataset based on an estimated DSGE model.
Trained ML algorithms on the dataset for forecasting.
Applied trained models to actual historical data for directional predictions.
The XGBoost model achieved 90% accuracy in debt forecasting.
XGBoost outperformed the elastic net model used as a benchmark.
Findings indicate that combining structural models with machine learning improves forecasting accuracy.

Abstract

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.

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