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非中心化随机波动率下结构向量自回归的部分识别AI 解读

We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails—unlike the common centred parameterization. This feature makes it well suited for…

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AI 解读

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…