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If we fit aγ-vector stationary time series using observationsx(1), ...,x(T) with AR models $$x(t) + a_k^{(T)} (1)x(t - 1) + \cdots + a_k^{(T)} (k)x(t - k) = \tilde \varepsilon (t)$$ , then the spectral densityf(λ) of {x(t)} can be estimated byf k (T) (λ)=(2π)− A k (T) (e −λ)−1∑ k (T) A k (T) (e −iλ)−⋆, where $$A_k^{(T)} (z) = \sum\limits_0^k {a_k^{(T)} (j)z^j ,\Sigma _k^{(T)} }$$ are estimates of the variance matrixΣ ofε(t), the residuals of the best linear prediction. By extending some results for the scalar case, this paper treats the asymptotic properties of the estimates in the multichannel case.
Acta Mathematicae Applicatae Sinica – Springer Journals
Published: Jul 13, 2005
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