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Citation Information : Statistics in Transition New Series. Volume 21, Issue 5, Pages 133-149, DOI: https://doi.org/10.21307/stattrans-2020-059
License : (CC BY-NC-ND 4.0)
Received Date : 28-March-2018 / Accepted: 04-May-2020 / Published Online: 20-December-2020
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.
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