Bayesian Prediction of Moving Average Processes Using Different Types of Priors | ||||
| The Egyptian Statistical Journal | ||||
| Article 3, Volume 62, Issue 1, June 2018, Page 35-53 PDF (4.76 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/esju.2018.244260 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Samir Shaarawy1; Emad Soliman2; Heba E.A. Shahin* 3 | ||||
| 1Department of Quantitative Methods and Information Systems. Kuwait University. Kuwait | ||||
| 2Department of Statistics, Faculty of Science, King Abdulaziz University. Kingdom of Saudi Arabia | ||||
| 3Department of Statistics, Research Sector. Central Bank of Egypt | ||||
| 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 | ||||
| Forecasting; Predictive density; Moving average Process; Natural conjugate prior | ||||
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