Generation Of Time Series Models With Given Spectral Properties
Georgi N. Boshnakov, School of Mathematics, The University of Manchester, Oxford Road, Manchester M13 9PL, UK. E‐mail: georgi.boshnakov@manchester.ac.uk;
Bisher M. Iqelan, Main Building/B333, Department of Mathematics, The Islamic University of Gaza, PO BOX 108, Gaza‐AlRimal, Palestine. E‐mail: biqelan@iugaza.edu.ps
Abstract
Abstract. We give a method for generation of periodically correlated and multivariate ARIMA models whose dynamic characteristics are partially or fully specified in terms of spectral poles and zeroes or their equivalents in the form of eigenvalues/eigenvectors of associated model matrices. Our method is based on the spectral decomposition of multi‐companion matrices and their factorization into products of companion matrices. Generated models are needed in simulation but may also be used in estimation, e.g. to set sensible initial values of parameters for nonlinear optimization.
We are not aware of any other general method for multivariate linear systems of comparable generality and control over the spectral properties of the generated model.
Citing Literature
Number of times cited according to CrossRef: 1
- Christoph Bergmeir, Mauro Costantini, José M. Benítez, On the usefulness of cross-validation for directional forecast evaluation, Computational Statistics & Data Analysis, 10.1016/j.csda.2014.02.001, 76, (132-143), (2014).
