Lag order selection for an optimal autoregressive covariance matrix estimator

A good parametric spectral estimator requires an accurate estimate of the sum ofAR 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.