Decomposition of time series models in state-space form

Authors:
E. J. Godolphin;Kostas Triantafyllopoulos
Affiliations:
Department of Mathematics, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK;School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

A methodology is proposed for decompositions of a very wide class of time series, including normal and non-normal time series, which are represented in state-space form. In particular the linked signals generated from dynamic generalized linear models are decomposed into a suitable sum of noise-free dynamic linear models. A number of relevant general results are given and two important cases, consisting of normally distributed data and binomially distributed data, are examined in detail. The methods are illustrated by considering examples involving both linear trend and seasonal component time series.