Linear stochastic systems
Random signals and systems
Automatic Autocorrelation and Spectral Analysis
Automatic Autocorrelation and Spectral Analysis
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This paper is on the asymptotic behavior of the spectral density of finite autoregressive (AR) and moving average (MA) approximations for a wide sense stationary time series. We consider two aspects: convergence of spectral density of moving average and autoregressive approximations when the covariances are known and when they are estimated. Under certain mild conditions on the spectral density and the covariance sequence, it is shown that the spectral densities of both approximations converge in L"2 as the order of approximation increases. It is also shown that the spectral density of AR approximations converges at the origin under the same conditions. Under additional regularity assumptions, we show that similar results hold for approximations from empirical covariance estimates.