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Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling

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  • Trindade, A. Alexandre

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.

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  • Trindade, A. Alexandre, 2003. "Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i05).
    • Handle: RePEc:jss:jstsof:v:008:i05
    DOI: http://hdl.handle.net/10.18637/jss.v008.i05
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    Cited by:

    1. Brockwell, Peter J. & Davis, Richard A. & Trindade, A. Alexandre, 2004. "Asymptotic properties of some subset vector autoregressive process estimators," Journal of Multivariate Analysis, Elsevier, vol. 90(2), pages 327-347, August.

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