BAYESIAN-INFERENCE AND PREDICTION FOR MEAN AND VARIANCE SHIFTS IN AUTOREGRESSIVE TIME-SERIES

• 成果类型: Article
• 署名作者: MCCULLOCH, RE; TSAY, RS
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290788
• 发表日期: 1993
• 页码: 968-978
• 关键词: models

摘要

This article is concerned with statistical inference and prediction of mean and variance changes in an autoregressive time series. We first extend the analysis of random mean-shift models to random variance-shift models. We then consider a method for predicting when a shift is about to occur. This involves appending to the autoregressive model a probit model for the probability that a shift occurs given a chosen set of explanatory variables. The basic computational tool we use in the proposed analysis is the Gibbs sampler. For illustration, we apply the analysis to several examples.