An algorithm for computing the asymptotic Fisher information matrix for seasonal SISO models

Authors
Publication date 2004
Journal Journal of Time Series Analysis
Volume | Issue number 25 | 5
Pages (from-to) 627-648
Number of pages 22
Organisations
  • Faculty of Economics and Business (FEB) - Amsterdam School of Economics Research Institute (ASE-RI)
Abstract The paper presents an algorithm for computing the asymptotic Fisher information matrix of a possibly seasonal single-input single-output (SISO) time-series model. That matrix is a block matrix whose elements are basically integrals of rational functions over the oriented unit circle. The procedure makes use of the autocovariance or the cross-covariance function of two autoregressive processes based on the same noise. The algorithm also works when the input variable is omitted, the case of a seasonal ARMA model.
Document type Article
Published at https://doi.org/10.1111/j.1467-9892.2004.01863.x
Published at http://www.blackwell-synergy.com/toc/jtsa/25/5
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