Modelling Multiple Time Series: Achieving the Aims

Granville Tunnicliffe-Wilson () and Alex Morton
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Granville Tunnicliffe-Wilson: Lancaster University, Dept. of Mathematics and Statistics
Alex Morton: Lancaster University, Dept. of Mathematics and Statistics

A chapter in COMPSTAT 2004 — Proceedings in Computational Statistics, 2004, pp 527-538 from Springer

Abstract: Abstract We review the traditional aims and methodology of multiple time series modelling, and present some recent developments in the models available to achieve these aims, in the context of both regularly and irregularly sampled data. These models are analogues of the vector autoregressive process, based on the generalised shift, or Laguerre, operator. They form a subclass of vector autoregressive moving-average processes; they retain many of the attractive features of the standard vector AR model, but have an added dimension of flexibility, that leads to improvements in predictive ability.

Keywords: Cross-spectral analysis; extended autoregression; prediction; transfer functions (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2656-2_43

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DOI: 10.1007/978-3-7908-2656-2_43

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