Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: A. Alexandre Trindade
Title: Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling
Abstract: The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002), show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.

Page views:: 7245. Submitted: 2002-06-12. Published: 2003-02-03.
Paper: Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling     Download PDF (Downloads: 7571)
Supplements: bdt.f90: program code Download (Downloads: 1061; 4KB) bdt2.f90: program code Download (Downloads: 1110; 9KB)

DOI: 10.18637/jss.v008.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.