Adaptive Bayesian Shrinkage of High-Dimensional Panel VARs

Zhiruo Zhang, Firmin Doko Tchatoka and Qazi Haque

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: 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.

Keywords: dynamic interdependency; cross-sectional heterogeneity; Bayesian Lasso; variable selection; spike and slab prior; financial contagion (search for similar items in EconPapers)
JEL-codes: C32 C54 C55 E17 F41 (search for similar items in EconPapers)
Pages: 67 pages
Date: 2026-06
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2026-40

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