Bayesian inference and prediction for mean and variance shifts in autoregressive time series

Robert McCulloch, Ruey S. Tsay

Research output: Contribution to journalArticle

98 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)968-978
Number of pages11
JournalJournal of the American Statistical Association
Volume88
Issue number423
DOIs
StatePublished - 1993
Externally publishedYes

Fingerprint

Bayesian Prediction
Autoregressive Time Series
Bayesian inference
Probit Model
Mean Shift
Gibbs Sampler
Autoregressive Model
Statistical Inference
Prediction
Model

Keywords

  • Gibbs sampler
  • Outlier
  • Probit model
  • Random level-shift model
  • Random variance-shift model
  • Variance change

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Bayesian inference and prediction for mean and variance shifts in autoregressive time series. / McCulloch, Robert; Tsay, Ruey S.

In: Journal of the American Statistical Association, Vol. 88, No. 423, 1993, p. 968-978.

Research output: Contribution to journalArticle

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