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Identification of canonical models for vectors of time series: a subspace approach

Author

Listed:
  • Alfredo Garcia-Hiernaux

    (Universidad Complutense de Madrid)

  • Jose Casals

    (Universidad Complutense de Madrid)

  • Miguel Jerez

    (Universidad Complutense de Madrid)

Abstract

We propose a new method to specify linear models for vectors of time series with some convenient properties. First, it provides a unified modeling approach for single and multiple time series, as the same decisions are required in both cases. Second, it is scalable, meaning that it provides a quick preliminary model, which can be refined in subsequent modeling phases if required. Third, it is optionally automatic, because the specification depends on a few key parameters which can be determined either automatically or by human decision. And last, it is parsimonious, as it allows one to choose and impose a canonical structure by a novel estimation procedure. Several examples with simulated and real data illustrate its application in practice.

Suggested Citation

  • Alfredo Garcia-Hiernaux & Jose Casals & Miguel Jerez, 2024. "Identification of canonical models for vectors of time series: a subspace approach," Statistical Papers, Springer, vol. 65(3), pages 1493-1530, May.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:3:d:10.1007_s00362-023-01451-y
    DOI: 10.1007/s00362-023-01451-y
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