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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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/biomet/81.1.115

Authors


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Institution:
University of Oxford
Research group:
Econometrics
Oxford college:
Nuffield College
Department:
Social Sciences Division - Economics
Economic and Social Research Council More from this funder
Publisher:
Biometrika Trust
Journal:
Biometrika Journal website
Volume:
81
Issue:
1
Pages:
115-131
Publication date:
1994-03-05
DOI:
ISSN:
0006-3444
URN:
uuid:ab585c71-bb8d-4248-a9a5-9045c9112756
Local pid:
ora:2266

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