A Bayesian analysis of a change in the parameters of autoregressive time series. Issue 9 (21st October 2017) - Slama, Abdeldjalil; Saggou, Hafida
ABSTRACT: In this article, we consider a Bayesian analysis of a possible change in the parameters of autoregressive time series of known order p, AR( p ). An unconditional Bayesian test based on highest posterior density (HPD) credible sets is determined. The test is useful to detect a change in any one of the parameters separately. Using the Gibbs sampler algorithm, we approximate the posterior densities of the change point and other parameters to calculate the p -values that define our test.