Bayesian Analysis of Autoregressive Time Series via the Gibbs Sampler

Applications of the Gibbs Sampler in time series analysis are considered. We show that the sampler applies nicely to various problems in analyzing autoregressive processes and, in many cases, it enjoys certain advantages over the traditional methods. The problems considered include random level‐shift models, outliers and missing values. Real examples are used to illustrate the analysis.