Bayesian Analysis of Change Point Detection in Autoregressive Time Series Model with Markov Process States
In this paper, a Bayesian approach for modeling the structural of the multiple change-point detection is introduced. This model has a discrete variable with hidden state which follows a Markov chain process with unknown transition probabilities with beta and logit-normal priors. Then using the appropriate priors for change point model, the posterior distribution are obtained and using simulation studies, the implementation of proposed model will be discussed. Finally an application of this model for index of consumer prices for goods and services in urban areas of Iran (ICPUI) data, is presented.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.