BAYES INFERENCE VIA GIBBS SAMPLING OF AUTOREGRESSIVE TIME-SERIES SUBJECT TO MARKOV MEAN AND VARIANCE SHIFTS
BAYES INFERENCE VIA GIBBS SAMPLING OF AUTOREGRESSIVE TIME-SERIES SUBJECT TO MARKOV MEAN AND VARIANCE SHIFTS
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DOI：
10.2307/1391303
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发表时间：
1993-01-01
影响因子：
3
通讯作者：
CHIB, S
中科院分区：
文献类型：
--
作者：
ALBERT, JH;CHIB, S
我们研究了自回归时间序列模型，受状态切换。这些转移是由一个未观察到的双状态指标变量的结果决定的，该变量遵循一个具有未知转移概率的马尔可夫过程。在贝叶斯框架中，每个时间点一个未观察到的状态被视为缺失数据，然后通过吉布斯抽样的模拟工具进行分析。这种方法是方便的，因为给定状态的参数的条件后验分布和给定参数的状态的条件后验分布都有一种适合蒙特卡罗采样的形式。该方法很简单，并为所有感兴趣的参数生成边际后验分布。还得到了状态、未来观测值和残差在参数空间上的平均后验分布。几个真实和人工数据集以及弱先验信息的例子说明了该方法的有效性。
We examine autoregressive time series models that are subject to regime switching. These shifts are determined by the outcome of an unobserved two-state indicator variable that follows a Markov process with unknown transition probabilities. A Bayesian framework is developed in which the unobserved states, one for each time point, are treated as missing data and then analyzed via the simulation tool of Gibbs sampling. This method is expedient because the conditional posterior distribution of the parameters, given the states, and the conditional posterior distribution of the states, given the parameters, all have a form amenable to Monte Carlo sampling. The approach is straightforward and generates marginal posterior distributions for all parameters of interest. Posterior distributions of the states, future observations, and the residuals, averaged over the parameter space are also obtained. Several examples with real and artificial data sets and weak prior information illustrate the usefulness of the methodology.