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Fast Filtering and Smoothing for Multivariate State Space Models

Koopman,S.J.M.
; 
Durbin,J.
Abstract
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors while our approach treats each element of the observational vector individually. This strategy leads to computationally efficient methods for multivariate filtering and smoothing. Also, the treatment of the diffuse initial state vector in multivariate models is much simpler than existing methods. The paper presents details of relevant algorithms for filtering, prediction and smoothing. Proofs are provided. Three examples of multivariate models in statistics and economics are presented for which the new approach is particularly relevant.
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Pagination: 20
Date
1998
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Volume Title
Publisher
Econometrics
Collections
Tilburg University Research Output Collection
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18.pdf
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Keywords
Diffuse initialisation, Kalman filter, multivariate models, smoothing, state space, time series
Citation
Koopman, S J M & Durbin, J 1998 'Fast Filtering and Smoothing for Multivariate State Space Models' CentER Discussion Paper, vol. 1998-18, Econometrics, Tilburg.
URI
https://hdl.handle.net/20.500.14602/26449
License
info:eu-repo/semantics/restrictedAccess
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