Abstract
Abstract. A vector linear time series model is observed as the sum of a convolution of an unknown signal and an additive noise process. The main objective is the estimation or deconvolution of the signal when the spectra of the signal and noise processes are unknown. We prove the strong consistency of a class of nonparametric spectral estimators derived by maximizing a particular Gaussian likelihood function. We also study the mean square convergence of the finite‐sample deconvolution estimators as a function of the sample length T, the filter length M and the spectral bandwidth BT=LT/T.
Published Version
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