Identification of Canonical Models for Vectors of Time Series: A Subspace Approach
48 Pages Posted: 3 Mar 2015 Last revised: 12 Dec 2022
Date Written: March 3, 2021
Abstract
In this paper we propose a new method to specify linear models for vectors of time series with some convenient properties: First, it provides a unique 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 quickly a possibly crude but statistically adequate model, which can be refined in further modeling phases if required. Third, it is optionally automatic, meaning that the specification depends on a few key parameters that can be determined either automatically or by human decision. And last it is parsimonious, as it allows one to impose a canonical structure, which can be further simplified through exclusion constraints. Several examples with simulated and real data illustrate the practical application of this procedure and a MATLAB implementation is freely distributed through the Internet.
Keywords: System identification, canonical models, Kronecker indices, subspace methods, state-space models, Kalman filter
JEL Classification: C32, C51
Suggested Citation: Suggested Citation