A comparison of approximate Bayesian forecasting methods for non-Gaussian time series

被引：0

作者：

Settimi, R ^{[1
]}

Smith, JQ ^{[1
]}

机构：

[1] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England

来源：

JOURNAL OF FORECASTING | 2000年 / 19卷 / 02期

关键词：

dynamic generalized linear models; sequential approximation; Poisson time series; Gibbs sampling;

D O I：

10.1002/(SICI)1099-131X(200003)19:2<135::AID-FOR751>3.0.CO;2-2

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We present the results on the comparison of efficiency of approximate Bayesian methods for the analysis and forecasting of non-Gaussian dynamic processes. A numerical algorithm based on MCMC methods has been developed to carry out the Bayesian analysis of non-linear time series. Although the MCMC-based approach is not fast, it allows us to study the efficiency, in predicting future observations, of approximate propagation procedures that, being algebraic, have the practical advantage of being very quick. Copyright (C) 2000 John Wiley & Sons, Ltd.

引用

页码：135 / 148

页数：14

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