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Partial non-Gaussian time series models

Abstract:
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.

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Publisher copy:
10.1093/biomet/81.1.115

Authors


Neil Shephard More by this author
Journal:
Biometrika
Volume:
81
Publication date:
1994
DOI:
URN:
uuid:36c1a58a-e726-4b9e-b042-c6ed5ef1c297
Local pid:
oai:economics.ouls.ox.ac.uk:13900
Language:
English

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