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Article: Distributional Kalman filters for Bayesian forecasting and closed form recurrences

TitleDistributional Kalman filters for Bayesian forecasting and closed form recurrences
Authors
KeywordsBayesian forecasting
Causal analysis
Conjugate analysis
Dynamic linear models
Kalman filter
Issue Date2011
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/2966
Citation
Journal of Forecasting , 2011, v. 30 n. 1, p. 210-224 How to Cite?
AbstractOver the last 50 years there has been an enormous explosion in developing full distributional analogues of the Kalman filter. In this paper we explore how some of the second-order processes discovered by Kalman have their analogues in Bayesian state space models. Many of the analogues in the lierature need to be calculated using numerical methods like Markov chain Monte Carlo so they retain, or even enhance, the descriptive power of the Kalman filter, but at the cost of reduced transparency. However, if the analogues are drawn properly, elegant recurrence relationshipsa-like those of the Kalman filtera-can still be developed that apply, at least, for one-step-ahead forecast distributions. In this paper we explore the variety of ways such models have been built, in particular with respect to graphical time series models.
Persistent Identifierhttp://hdl.handle.net/10722/176511
ISSN
2015 Impact Factor: 0.818
2015 SCImago Journal Rankings: 0.706
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSmith, JQ-
dc.contributor.authorFreeman, G-
dc.date.accessioned2012-11-30T06:41:20Z-
dc.date.available2012-11-30T06:41:20Z-
dc.date.issued2011-
dc.identifier.citationJournal of Forecasting , 2011, v. 30 n. 1, p. 210-224-
dc.identifier.issn0277-6693-
dc.identifier.urihttp://hdl.handle.net/10722/176511-
dc.description.abstractOver the last 50 years there has been an enormous explosion in developing full distributional analogues of the Kalman filter. In this paper we explore how some of the second-order processes discovered by Kalman have their analogues in Bayesian state space models. Many of the analogues in the lierature need to be calculated using numerical methods like Markov chain Monte Carlo so they retain, or even enhance, the descriptive power of the Kalman filter, but at the cost of reduced transparency. However, if the analogues are drawn properly, elegant recurrence relationshipsa-like those of the Kalman filtera-can still be developed that apply, at least, for one-step-ahead forecast distributions. In this paper we explore the variety of ways such models have been built, in particular with respect to graphical time series models.-
dc.languageeng-
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/2966-
dc.relation.ispartofJournal of Forecasting-
dc.rightsJournal of Forecasting . Copyright © John Wiley & Sons Ltd.-
dc.rightsSpecial Statement for Preprint only Before publication: 'This is a preprint of an article accepted for publication in [The Journal of Pathology] Copyright © ([year]) ([Pathological Society of Great Britain and Ireland])'. After publication: the preprint notice should be amended to follows: 'This is a preprint of an article published in [include the complete citation information for the final version of the Contribution as published in the print edition of the Journal]' For Cochrane Library/ Cochrane Database of Systematic Reviews, add statement & acknowledgement : ‘This review is published as a Cochrane Review in the Cochrane Database of Systematic Reviews 20XX, Issue X. Cochrane Reviews are regularly updated as new evidence emerges and in response to comments and criticisms, and the Cochrane Database of Systematic Reviews should be consulted for the most recent version of the Review.’ Please include reference to the Review and hyperlink to the original version using the following format e.g. Authors. Title of Review. Cochrane Database of Systematic Reviews 20XX, Issue #. Art. No.: CD00XXXX. DOI: 10.1002/14651858.CD00XXXX (insert persistent link to the article by using the URL: http://dx.doi.org/10.1002/14651858.CD00XXXX) (This statement should refer to the most recent issue of the Cochrane Database of Systematic Reviews in which the Review published.)-
dc.subjectBayesian forecasting-
dc.subjectCausal analysis-
dc.subjectConjugate analysis-
dc.subjectDynamic linear models-
dc.subjectKalman filter-
dc.titleDistributional Kalman filters for Bayesian forecasting and closed form recurrencesen_US
dc.typeArticleen_US
dc.identifier.emailFreeman, G: gfreeman@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/for.1207-
dc.identifier.scopuseid_2-s2.0-78650783467-
dc.identifier.volume30-
dc.identifier.issue1-
dc.identifier.spage210-
dc.identifier.epage224-
dc.identifier.isiWOS:000286291800009-
dc.publisher.placeUnited Kingdom-

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