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Biometrika 1997 84(3):653-667; doi:10.1093/biomet/84.3.653
© 1997 by Biometrika Trust
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Likelihood analysis of non-Gaussian measurement time series

NEIL SHEPHARD and MICHAEL K. PITT

Nuffield College Oxford, OX1 1NF, U.K. e-mail: neil.shephard{at}nuf.ox.ac.uk
Department of Statistics, University of Oxford OX1 3TG, U.K. e-mail: michael.pitt{at}nuf.ox.ac.uk

In this paper we provide methods for estimating non-Gaussian time series models. These techniques rely on Markov chain Monte Carlo to carry out simulation smoothing and Bayesian posterior analysis of parameters, and on importance sampling to estimate the likelihood function for classical inference. The time series structure of the models is used to ensure that our simulation algorithms are efficient.

Key Words: Blocking • Exponential family • Importance sampling • Markov chain Monte Carlo • Simulation smoother • Stochastic volatility


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