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314306

Bayesian Prediction of Autoregressive Models Using Different Types of Priors

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Last updated: 05 Jan 2025

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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.  

DOI

10.21608/esju.2010.314306

Keywords

Forecasting, Prediction - One Step-Ahead Predictive Density - Autoregressive Process - G-Prior - Jeffreys' Prior - Natural Conjugate Prior - Informative Prior - Noninformative Prior

Volume

54

Article Issue

2

Related Issue

43101

Issue Date

2010-12-01

Publish Date

2010-12-01

Page Start

108

Page End

126

Print ISSN

0542-1748

Online ISSN

2786-0086

Link

https://esju.journals.ekb.eg/article_314306.html

Detail API

https://esju.journals.ekb.eg/service?article_code=314306

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4

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Original Article

Type Code

1,914

Publication Type

Journal

Publication Title

The Egyptian Statistical Journal

Publication Link

https://esju.journals.ekb.eg/

MainTitle

Bayesian Prediction of Autoregressive Models Using Different Types of Priors

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Article

Created At

28 Dec 2024