Bayesian Prediction of Moving Average Processes Using Different Types of Priors

Shaarawy, Samir; Soliman, Emad; Shahin, Heba E.A.

doi:10.21608/esju.2018.244260

Bayesian Prediction of Moving Average Processes Using Different Types of Priors

Document Type : Original Article

Authors

¹ Department of Quantitative Methods and Information Systems. Kuwait University. Kuwait

² Department of Statistics, Faculty of Science, King Abdulaziz University. Kingdom of Saudi Arabia

³ Department of Statistics, Research Sector. Central Bank of Egypt

10.21608/esju.2018.244260

Abstract

The current article approaches the Bayesian prediction of moving average processes using three well-known priors: g prior, natural conjugate (NC) prior, and Jeffreys' prior. The main goal of the study is to derive approximate one step-ahead predictive densities for moving average (MA) processes using each of the above-mentioned priors. However, the basic contribution is the derivation of the predictive density based upon the g prior. Investigating the performance of the three one step-ahead predictive densities is performed via comprehensive simulation studies using MA(1) and MA(2) processes for illustration. The simulation results show the equivalence of the performance of the three one step-ahead predictive densities based on the three considered priors in the forecasting process.

Keywords