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On the convergence of finite linear predictors of stationary processes

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  • Pourahmadi, Mohsen

Abstract

It is shown that the finite linear least-squares predictor of a multivariate stationary process converges to its Kolmogorov-Wiener predictor at an exponential rate, provided that the entries of its spectral density matrix are smooth functions. Also, the same rate of convergence holds for the partial sums of the Kolmogorov-Wiener predictor.

Suggested Citation

  • Pourahmadi, Mohsen, 1989. "On the convergence of finite linear predictors of stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 30(2), pages 167-180, August.
    • Handle: RePEc:eee:jmvana:v:30:y:1989:i:2:p:167-180
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    Cited by:

    1. Raymond Cheng & Charles B. Harris, 2015. "Mixed-Norm Spaces and Prediction of SαS Moving Averages," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 853-875, November.
    2. Datta Gupta, Syamantak & Mazumdar, Ravi R. & Glynn, Peter, 2013. "On the convergence of the spectrum of finite order approximations of stationary time series," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 1-21.

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