Abstract
Abstract This paper discusses the analysis of non-Gaussian time series using state space models from both classical and Bayesian points of view. A major advantage of the state space approach is that we can model the behaviour of different components of the series separately and then put the submodels together to form an overall model for the series. State space models are very general and can handle a remarkably wide range of applications ranging from autoregressive integrated moving average models and unobserved components time series models to smoothing models with roughness penalties.
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