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Distributional Kalman filters for Bayesian forecasting and closed form recurrences

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Smith, J. Q., 1953- and Freeman, Guy. (2011) Distributional Kalman filters for Bayesian forecasting and closed form recurrences. Journal of Forecasting, Vol.30 (No.1). pp. 210-224. ISSN 0277-6693

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Abstract

Over 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 relationships like those of the Kalman filter 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. Copyright (C) 2010 John Wiley & Sons, Ltd.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Kalman filtering, State-space methods
Journal or Publication Title: Journal of Forecasting
Publisher: John Wiley & Sons Ltd.
ISSN: 0277-6693
Date: January 2011
Volume: Vol.30
Number: No.1
Page Range: pp. 210-224
Identification Number: 10.1002/for.1207
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
References:
URI: http://wrap.warwick.ac.uk/id/eprint/41653

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