An efficient Bayesian approach to multiple structural change in multivariate time series
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This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Due to conjugate prior assumptions we obtain a very efficient sampler for the regime allocation variable. A new hierarchical prior is introduced to allow for learning over different structural breaks. The model is extended to
independent breaks in regression coefficients and the volatility parameters.Two empirical applications show
the improvements the model has over benchmarks. In a macro application with 7 variables we empirically demonstrate
the benefits from moving from a multivariate structural break model to a set of univariate structural break models to account for heterogeneous break patterns across data series.
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