Bayesian Inference and Prediction for Mean and Variance Shifts in Autoregressive Time Series
作者:
RobertE. McCulloch,
RueyS. Tsay,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1993)
卷期:
Volume 88,
issue 423
页码: 968-978
ISSN:0162-1459
年代: 1993
DOI:10.1080/01621459.1993.10476364
出版商: Taylor & Francis Group
关键词: Gibbs sampler;Outlier;Probit model;Random level-shift model;Random variance-shift model;Variance change
数据来源: Taylor
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.
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