A Monte Carlo Markov chain algorithm for a class of mixture time series models

  • Authors:

  • John W. Lau;Mike K. So

  • Affiliations:

  • School of Mathematics and Statistics, University of Western Australia, Perth, Australia;Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

  • Venue:
  • Statistics and Computing
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

This article generalizes the Monte Carlo Markov Chain (MCMC) algorithm, based on the Gibbs weighted Chinese restaurant (gWCR) process algorithm, for a class of kernel mixture of time series models over the Dirichlet process. This class of models is an extension of Lo's (Ann. Stat. 12:351---357, 1984) kernel mixture model for independent observations. The kernel represents a known distribution of time series conditional on past time series and both present and past latent variables. The latent variables are independent samples from a Dirichlet process, which is a random discrete (almost surely) distribution. This class of models includes an infinite mixture of autoregressive processes and an infinite mixture of generalized autoregressive conditional heteroskedasticity (GARCH) processes.