Affiliations: [a] Department of Management, Invertis University, Bareilly, India | [b] Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India | [c] Department of Statistics, Institute of Science, Banaras Hindu University, Varanasi, India | [d] Department of Community Medicine, Jawaharlal Nehru Medical College, Ajmer, India
Correspondence:
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Corresponding author: Jitendra Kumar, Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India. E-mail: [email protected].
Abstract: This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.
