A Bayesian Analysis of a Structural Change in the Parameters of a Time Series

Abstract The purpose of this paper is to make a Bayesian analysis of a first-order autoregressive process subject to one change in both the variance of the error terms and the autocorrelation coefficients at an unknown time point. The main emphasis is to derive the posterior distributions of the change point, the autocorrelation parameter and the variance ratio. A numerical illustration is provided using the Gibbs sampler.

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