文章基本信息

标题：Comparison of MCMC Methods for Estimating GARCH Models
本地全文：下载
作者：Manabu Asai
期刊名称：JOURNAL OF THE JAPAN STATISTICAL SOCIETY
印刷版ISSN：1882-2754
电子版ISSN：1348-6365
出版年度：2006
卷号：36
期号：2
页码：199-212
DOI：10.14490/jjss.36.199
出版社：JAPAN STATISTICAL SOCIETY
摘要：This paper reviews several MCMC methods for estimating the class of ARCH models, and compare performances of them. With respect to the mixing, efficiency and computational requirement of the MCMC, this paper found the best method is the tailored approach based on the acceptance-rejection Metropolis-Hastings algorithm.
关键词：Bayesian inference;GARCH;Gibbs sampler;Markov chain Monte Carlo;Metropolis-Hastings algorithm