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

We develop a ridge-regularized Factor-Augmented Vector Autoregression (FAVAR) framework for modeling macro-fiscal dynamics in settings where the number of predictors is large relative to the available sample. Rather than relying on an unrestricted factor decomposition, the high-dimensional information set is summarized through two economically interpretable latent factors, namely a real activity factor and a nominal-financial factor, extracted from separate data blocks. These factors are embedded in a regularized VAR together with key observable macro-fiscal variables, including real GDP growth, inflation, the primary balance, and the interest-rate-growth differential, allowing macroeconomic conditions to be linked to sovereign debt dynamics through a nonlinear accounting identity. In an application to Greece, the extracted factors capture major business-cycle, inflationary, and financing episodes, while impulse responses are economically plausible and dynamically stable. Recursive out-of-sample forecasting exercises show that the model performs comparatively well for inflation and financing conditions at selected horizons, while simple autoregressive benchmarks remain difficult to outperform for persistent real activity variables. Overall, the results highlight the usefulness of combining structured factor extraction with ridge regularization to obtain a parsimonious and interpretable framework for macro-fiscal modelling, scenario analysis, and debt sustainability assessment.