Granger Causality and Regime Inference in Markov Switching VAR Models with Bayesian Methods
马尔可夫转换 VAR 模型的格兰杰因果与状态推断：贝叶斯方法
In this paper, we derive restrictions for Granger noncausality in MS-VAR models and show under what conditions a variable does not affect the forecast of the hidden Markov process. To assess the noncausality hypotheses, we apply Bayesian inference. The computational tools include a novel block Metropolis–Hastings sampling algorithm for the estimation of the underlying models. We analyze a system of monthly US data on money and income. The results of testing in MS-VARs…