An efficient Bayesian approach to multiple structural change in multivariate time series
提出一种高效贝叶斯方法，用于向量自回归等多元模型的多重结构断点估计和预测，通过共轭先验和分层先验实现，并在宏观数据应用中优于基准模型。
Summary This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Owing 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 seven 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.