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Modelling Multiple Time Series: Achieving the Aims

In: COMPSTAT 2004 — Proceedings in Computational Statistics

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  • Granville Tunnicliffe-Wilson

    (Lancaster University, Dept. of Mathematics and Statistics)

  • Alex Morton

    (Lancaster University, Dept. of Mathematics and Statistics)

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.

Suggested Citation

  • Granville Tunnicliffe-Wilson & Alex Morton, 2004. "Modelling Multiple Time Series: Achieving the Aims," Springer Books, in: Jaromir Antoch (ed.), COMPSTAT 2004 — Proceedings in Computational Statistics, pages 527-538, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2656-2_43
    DOI: 10.1007/978-3-7908-2656-2_43
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