RT Journal Article
T1 Identification of Canonical Models for Vectors of Time Series: A Subspace Approach
A1 García Hiernaux, Alfredo Alejandro
A1 Casals, José
A1 Jerez Méndez, Miguel
AB 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.
PB Springer Verlag
SN 0932-5026
YR 2023
FD 2023
LK https://hdl.handle.net/20.500.14352/72171
UL https://hdl.handle.net/20.500.14352/72171
LA eng
DS Docta Complutense
RD 6 abr 2025