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

Alfredo Garcia-Hiernaux, Jose Casals () and Miguel Jerez ()
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Jose Casals: Universidad Complutense de Madrid
Miguel Jerez: Universidad Complutense de Madrid

Statistical Papers, 2024, vol. 65, issue 3, No 13, 1493-1530

Abstract: 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.

Keywords: System identification; Canonical models; Kronecker indices; Subspace methods; State-space models (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01451-y

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