Bayesian Change Point Detection in VAR Processes

We study Bayesian inference for structural breaks in vector autoregressive (VAR) models, where both contemporaneous and dynamic interdependencies complicate regime identification. Within this framework, we extend the asymptotic analysis of Shimizu to multiple dependent series and establish conditions under which the posterior distribution of the change-point location under conjugate priors asymptotically concentrates on the true break. Consistency is further demonstrated for non-conjugate priors, and the results are extended to multiple change-points under controlled overlaps between estimated and true regimes. A data-driven hyperparameter specification is suggested to automate prior selection. Simulation studies confirm the accurate recovery of structural breaks, and empirical applications to U.S. macroeconomics data illustrate the method's advantages over the existing approaches.