Partial non-Gaussian time series models

In this paper we suggest the use of simulation techniques to extend the applicability of the usual Gaussian state space filtering and smoothing techniques to a class of non-Gaussian time series models. This allows a fully Bayesian or maximum likelihood analysis of some interesting models, including outlier models, discrete Markov chain components, multiplicative models and stochastic variance models. Finally we discuss at some length the use of a non-Gaussian model to seasonally adjust the published money supply figures.The full-text of this article is not currently available in ORA. Citation: Shephard, N. (1994). 'Partial Non-Gaussian State Space', Biometrika, 81(1), 115-131. [Available at http://biomet.oxfordjournals.org/]