A Bayesian analysis of complete multiple breaks in a panel autoregressive (CMB-PAR(1)) time series model

Varun  Agiwal; Jitendra Kumar; Dahud Kehinde  Shangodoyin

doi:10.21307/stattrans-2020-059

A Bayesian analysis of complete multiple breaks in a panel autoregressive (CMB-PAR(1)) time series model

Varun Agiwal Department of Community Medicine, Jawaharlal Nehru Medical College, Ajmer, India ; Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India ORCID:https://orcid.org/0000-0003-1955-8832; https://orcid.org/0000-0003-4473-4148 , Jitendra Kumar Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India ORCID:https://orcid.org/0000-0003-4473-4148 , Dahud Kehinde Shangodoyin Department of Statistics, University of Botswana, Gaborone, Botswana ORCID:https://orcid.org/0000-0002-0449-9510 Statistics in Transition new series, vol. 21, 2020, 5, pages: 133-149 Published online: 4 December 2020 DOI 10.21307/stattrans-2020-059

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ABSTRACT

Most economic time series, such as GDP, real exchange rate and banking series are irregular by nature as they may be affected by a variety of discrepancies, including political changes, policy reforms, import-export market instability, etc. When such changes entail serious consequences for time series modelling, various researchers manage this problem by applying a structural break. Thus, the aim of this paper is to develop a generalised structural break time series model. The paper discusses a panel autoregressive model with multiple breaks present in all parameters, i.e. in the autoregressive coefficient and mean and error variance, which is a generalisation of various sub-models. The Bayesian approach is applied to estimate the model parameters and to obtain the highest posterior density interval. Strong evidence is observed to support the Bayes estimator and then it is compared with the maximum likelihood estimator. A simulation experiment is conducted and an empirical application on the SARRC association’s GDP per capita time series is used to illustrate the performance of the proposed model. This model is also extended to a temporary shift model. Key words: panel autoregressive model, structural break, MCMC, posterior probability.

KEYWORDS

panel autoregressive model, structural break, MCMC, posterior probability

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