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Spectral estimation for high-dimensional linear processes
We develop a random-matrix-based framework for estimating the latent joint spectral distribution of the coefficient matrices and innovation covariance of a class of high-dimensional linear processes from the eigenvalues of weighted sample periodograms. The model assumes that these matrices share a c
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