On Bayesian Identification of Autoregressive Processes

Emad El-Din Abdel-Salam Soliman, Samir Mostafa Shaarawy, Waseem W. Sorour

Abstract


The main objective of the current study is to handle the identification problem of autoregressive processes from the Bayesian point of view. Two Bayesian identification approaches are considered. They are referred to as the direct and the indirect approaches. The two approaches are employed to solve the Bayesian identification problem of autoregressive processes using three well known priors. These priors are the G prior, the Natural-Conjugate prior and Jeffrey's prior. The theoretical derivations related to the two Bayesian identification approaches are conducted using the above mentioned priors. Moreover, the performance of the two techniques, using each of the three priors, is investigated via comprehensive simulation studies. Simulation results show that the two techniques are adequate to solve the identification problem of autoregressive processes. The increase in the time series length leads to better performance for each technique. The use of different priors doesn't affect the previous results.


Keywords


Autoregressive processes, Bayesian time series identification

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DOI: http://dx.doi.org/10.18187/pjsor.v11i1.709

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Title

On Bayesian Identification of Autoregressive Processes

Keywords

Autoregressive processes, Bayesian time series identification

Description

The main objective of the current study is to handle the identification problem of autoregressive processes from the Bayesian point of view. Two Bayesian identification approaches are considered. They are referred to as the direct and the indirect approaches. The two approaches are employed to solve the Bayesian identification problem of autoregressive processes using three well known priors. These priors are the G prior, the Natural-Conjugate prior and Jeffrey's prior. The theoretical derivations related to the two Bayesian identification approaches are conducted using the above mentioned priors. Moreover, the performance of the two techniques, using each of the three priors, is investigated via comprehensive simulation studies. Simulation results show that the two techniques are adequate to solve the identification problem of autoregressive processes. The increase in the time series length leads to better performance for each technique. The use of different priors doesn't affect the previous results.


Date

2015-04-06

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 11 No. 1, 2015



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810