Main Article Content

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

Article Details

Author Biography

Emad El-Din Abdel-Salam Soliman, associate professor of Statistics - King Abul-Aziz University - Saudi Arabia

Department of Statistics, Faculty od Science, King Abdul-Aziz university - Saudi Arabia
How to Cite
Soliman, E. E.-D. A.-S., Shaarawy, S. M., & Sorour, W. W. (2015). On Bayesian Identification of Autoregressive Processes. Pakistan Journal of Statistics and Operation Research, 11(1), 11-28. https://doi.org/10.18187/pjsor.v11i1.709