Lag order selection for an optimal autoregressive covariance matrix estimator
AbstractA good parametric spectral estimator requires an accurate estimate of the sum of AR coefficients, however a criterion which minimizes the innovation variance not necessarily yields the best spectral estimate. This paper develops an alternative information criterion considering the bias in the sum of the parameters for the autoregressive estimator of the spectral density at frequency zero.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.
Volume (Year): 37 (2010)
Issue (Month): 5 ()
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