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Maximum likelihood recursive least squares estimation for multivariate equation-error ARMA systems
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DOI：10.1016/j.jfranklin.2018.07.041.png)
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Abstract
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This paper focuses on the parameter estimation problems of multivariate equation-error systems. A recursive generalized extended least squares algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification models, each of which has only a parameter vector, and a coupled subsystem maximum likelihood recursive least squares identification algorithm is developed for estimating the parameter vectors of these submodels. The simulation example shows that the proposed algorithm is effective and has high estimation accuracy. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
PARAMETER-ESTIMATION ALGORITHM
THRESHOLD DIVIDEND STRATEGY
MOVING AVERAGE NOISE
IDENTIFICATION ALGORITHMS
DYNAMICAL-SYSTEMS
MULTI-INNOVATION
MODEL
PERFORMANCE
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