Journal article
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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Authors
Funding
Economic and Social Research Council
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Bibliographic Details
- Publisher:
- Biometrika Trust
- Journal:
- Biometrika Journal website
- Volume:
- 81
- Issue:
- 1
- Pages:
- 115-131
- Publication date:
- 1994-03-01
- DOI:
- ISSN:
-
0006-3444
Item Description
- Language:
- English
- Keywords:
- Subjects:
- UUID:
-
uuid:ab585c71-bb8d-4248-a9a5-9045c9112756
- Local pid:
- ora:2266
- Deposit date:
- 2008-08-12
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Terms of use
- Copyright holder:
- Biometrika Trust
- Copyright date:
- 1994
- Notes:
- 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/].
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