Refined instrumental variable estimation:maximum likelihood optimization of a unified Box-Jenkins model

Young, Peter (2015) Refined instrumental variable estimation:maximum likelihood optimization of a unified Box-Jenkins model. Automatica, 52. pp. 35-46. ISSN 0005-1098

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Abstract

For many years, various methods for the identification and estimation of parameters in linear, discrete-time transfer functions have been available and implemented in widely available Toolboxes for Matlab. This paper considers a unified Refined Instrumental Variable (RIV) approach to the estimation of discrete and continuous-time transfer functions characterized by a unified operator that can be interpreted in terms of backward shift, derivative or delta operators. The estimation is based on the formulation of a pseudo-linear regression relationship involving optimal prefilters that is derived from an appropriately unified Box-Jenkins transfer function model. The paper shows that, contrary to apparently widely held beliefs, the iterative RIV algorithm provides a reliable solution to the maximum likelihood optimization equations for this class of Box-Jenkins transfer function models and so its en bloc or recursive parameter estimates are optimal in maximum likelihood, prediction error minimization and instrumental variable terms.

Item Type:
Journal Article
Journal or Publication Title:
Automatica
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
73320
Deposited By:
Deposited On:
18 Mar 2015 11:46
Refereed?:
Yes
Published?:
Published
Last Modified:
11 Jun 2020 03:37