Adaptive Bayesian Shrinkage of High-Dimensional Panel VARs

We develop a Bayesian framework that combines adaptive shrinkage with variable selection to address over-parameterisation and sparsity in high-dimensional panel vector autoregressions (PVARs). The proposed approach employs Laplace-based spike-and-slab priors to enable flexible modelling of dynamic cross-sectional interdependencies and unit-specific heterogeneity. Monte Carlo evidence shows that the method delivers improvements in estimation accuracy and forecasting performance relative to existing regularisation approaches. We illustrate its empirical relevance in two applications. The first investigates financial contagion in euro area sovereign bond markets, while the second examines international forecasting performance in a multi-country macroeconomic panel. The results highlight the benefits of adaptive, component-specific shrinkage for capturing heterogeneous spillover structures in complex panel systems.