A Shrinkage Factor-Augmented VAR for High-Dimensional Macro–Fiscal Dynamics

We propose a ridge-regularized Factor-Augmented Vector Autoregression (FAVAR) for forecasting macro–fiscal systems in data-rich environments where the cross-sectional dimension is large relative to the available sample. The framework combines principal-component factor extraction with a shrinkage-based VAR for the joint dynamics of observed macro–fiscal variables and latent components. Applying the model to Greece, we show that the extracted factors capture meaningful real and nominal structures, while the ridge-regularized VAR delivers stable impulse responses and coherent short- and medium-term dynamics for variables central to the sovereign debt identity. A recursive out-of-sample evaluation indicates that the ridge-FAVAR systematically improves medium-term forecasting accuracy relative to standard AR benchmarks, particularly for real GDP growth and the interest–growth differential. The results highlight the usefulness of shrinkage-augmented factor models for macro–fiscal forecasting and motivate further econometric work on regularized state-space and structural factor VARs.