Bayesian Prediction of Autoregressive Models Using Different Types of Priors | ||||
| The Egyptian Statistical Journal | ||||
| Article 4, Volume 54, Issue 2, December 2010, Page 108-126 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/esju.2010.314306 | ||||
 View on SCiNiTO | ||||
| Abstract | ||||
| The current study approaches the Bayesian prediction of autoregressive processes using three well-known priors; g-prior, natural conjugate (NC)) prior, and Jeffreys' prior. The main goal of the study is to derive the one step-ahead predictive densities in case of autoregressive (AR) models 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 on step-ahead predictive densities is performed via simulation studies using AR (1) and AR (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; Prediction - One Step-Ahead Predictive Density - Autoregressive Process - G-Prior - Jeffreys' Prior - Natural Conjugate Prior - Informative Prior - Noninformative Prior | ||||
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Statistics Article View: 29 |
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