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 language | English (US) |
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Pages (from-to) | 968-978 |
Number of pages | 11 |
Journal | Journal of the American Statistical Association |
Volume | 88 |
Issue number | 423 |
DOIs | |
State | Published - Sep 1993 |
Externally published | Yes |
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