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

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>02/2015
<mark>Journal</mark>Automatica
Volume52
Number of pages12
Pages (from-to)35-46
Publication StatusPublished
<mark>Original language</mark>English

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.