Paper Info
Title
A New Multivariate Equation-Error Autoregressive Moving Average System With Conditional Heteroscedastic Noise: Maximum Likelihood Identification
Abstract
The aim of this paper is to deal with the problem of conditional heteroscedastic noise in multivariate systems. In this regard, a new multivariate equation-error system with colored noise is introduced, in which the noise conditional variance varies with time and the noise exhibits a GARCH process. Thus, in this our new approach, both the time dependency and probability distribution of noise samples shall be identified. Based on the maximum likelihood principle and orthogonality of parameters, the problem of multivariate system identification is reduced into two separate maximum likelihood problems. An iterative algorithm is proposed, which can efficiently estimate the problem parameters based on gradient reduction and nonlinear optimization. Besides, a bootstrap algorithm is applied for assessing the accuracy of estimators. Also, some asymptotic result of proposed method in large sample size is provided. According to bootstrap simulation results, the proposed algorithm can effectively estimate the parameters of the system with conditional heteroscedastic noise and provides more accurate parameter estimates with a much lower confidence interval, compared to the least square method. (C) 2021 Elsevier Inc. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.dsp.2021.103213
DIGITAL SIGNAL PROCESSING
Keywords
DocType
Volume
Multi-input multi-output system, Time-varying conditional variance, Parameter estimation, Maximum likelihood, GARCH process, Bootstrapping
Journal
118
ISSN
Citations 
PageRank 
1051-2004
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Hamidreza Hakimdavoodi100.34
Maryam Amirmazlaghani200.68
hamidreza amindavar321536.34