A Novel Approach to Parametrization and Parameter Estimation in Linear Dynamic Systems

Manfred Deistler (), Thomas Ribarits () and Bernard Hanzon ()
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Manfred Deistler: Vienna University of Technology, Institute for Mathematical Methods in Economics, Research Unit Econometrics and System Theory (EOS)
Bernard Hanzon: Leiden University, Mathematical Institute

A chapter in COMPSTAT 2004 — Proceedings in Computational Statistics, 2004, pp 137-147 from Springer

Abstract: Abstract We describe a novel approach, called data driven local coordinates (DDLC), for parametrizing linear systems in state space form, and we analyze some of its properties which are relevant for e.g. maximum likelihood estimation. In addition we describe how this idea can be used for a concentrated likelihood function, obtained by a least squares type concentration step, which gives the so called sls (separable least squares) DDLC approach. Both approaches give favourable results in numerically optimizing the likelihood function in simulation studies.

Keywords: Identification; parametrization; multivariate state space systems (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2656-2_10

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DOI: 10.1007/978-3-7908-2656-2_10

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