A BAYESIAN INFERENCE OF MULTIPLE STRUCTURAL BREAKS IN MEAN AND ERROR VARIANCE IN PANELAR (1) MODEL

Agiwal Varun (), Kumar Jitendra () and Shangodoyin Dahud Kehinde ()
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Agiwal Varun: Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India
Kumar Jitendra: Department of Statistics, Central University of Rajasthan, Bandersindri, Ajmer, India
Shangodoyin Dahud Kehinde: Department of Statistics, University of Botswana, Gaborone, Botswana

Statistics in Transition New Series, 2018, vol. 19, issue 1, 7-23

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. (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:19:y:2018:i:1:p:7-23:n:8

DOI: 10.21307/stattrans-2018-001

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