Fast Filtering and Smoothing for Multivariate State Space Models
AbstractThis 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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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1998-18.
Date of creation: 1998
Date of revision:
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Web page: http://center.uvt.nl
Diffuse initialisation; Kalman filter; multivariate models; smoothing; state space; time series;
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