A Bayesian inference of multiple  structural breaks in mean and error variance in Panel AR (1) model .

Varun  Agiwal; Jitendra  Kumar; Dahud Kehinde  Shangodoyin

doi:https://doi.org/10.21307/stattrans-2018-001

A Bayesian inference of multiple structural breaks in mean and error variance in Panel AR (1) model .

Varun Agiwal Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India. , Jitendra Kumar Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India. , Dahud Kehinde Shangodoyin Department of Statistics, University of Botswana, Gaborone, Botswana. Statistics in Transition new series, vol. 19, 2018, 1, pages: 7-24 Published online: 1 March 2018 https://doi.org/10.21307/stattrans-2018-001

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ABSTRACT

This paper explores the effect of multiple structural breaks to estimate the parameters and test the unit root hypothesis in panel data time series model under Bayesian perspective. These breaks are present in both mean and error variance at the same time point. We obtain Bayes estimates for different loss function using conditional posterior distribution, which is not coming in a closed form, and this is approximately explained by Gibbs sampling. For hypothesis testing, posterior odds ratio is calculated and solved via Monte Carlo Integration. The proposed methodology is illustrated with numerical examples.

KEYWORDS

panel data model, autoregressive model, structural break, MCMC, posterior odds ratio

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