Bayesian modelling of ARFIMA processes by Markov chain Monte Carlo methods

被引：0

作者：

Pai, JS ^{[1
]}

Ravishanker, N ^{[1
]}

机构：

[1] UNIV CONNECTICUT,DEPT STAT,STORRS,CT 06269

来源：

JOURNAL OF FORECASTING | 1996年 / 15卷 / 02期

关键词：

ARFIMA models; exact likelihood; model selection; partial linear regression coefficients;

D O I：

10.1002/(SICI)1099-131X(199603)15:2<63::AID-FOR606>3.0.CO;2-5

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This article describes Bayesian inference for autoregressive fractionally integrated moving average (ARFIMA) models using Markov chain Monte Carlo methods. The posterior distribution of the model parameters, corresponding to the exact likelihood function is obtained through the partial linear regression coefficients of the ARFIMA process. A Metropolis-Rao-Blackwellizallization approach is used for implementing sampling-based Bayesian inference. Bayesian model selection is discussed and implemented.

引用

页码：63 / 82

页数：20

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