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The purpose of this paper is to provide a systematic treatment of the problem of estimation in systems of linear stochastic equations where some of the disturbances are uncorrelated and where the identifying equations are equivalent to a linear system.

Adaptations of the classical Wiener–Kolmogorov filters are described that enable them to be applied to short nonstationary sequences. Alternative filtering methods that operate in the time domain and the frequency domain are described. The frequency-domain methods have the advantage of allowing components of the data to be separated along sharp dividing lines in the frequency domain, without incurring any leakage. The paper contains a novel treatment of the start-up problem that affects the filtering of trended data sequences.

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Matrix Differential CalculusJan R. Magnus and Heinz Neudecker John Wiley and Sons, 1988 - Linear StructuresJan R. Magnus Charles Griffin and Co., 1988

The Estimation of Linear Stochastic Models with Covariance Restrictions

WIENER–KOLMOGOROV FILTERING, FREQUENCY-SELECTIVE FILTERING, AND POLYNOMIAL REGRESSION

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3 results

Matrix Differential CalculusJan R. Magnus and Heinz Neudecker John Wiley and Sons, 1988 - Linear StructuresJan R. Magnus Charles Griffin and Co., 1988

The Estimation of Linear Stochastic Models with Covariance Restrictions

WIENER–KOLMOGOROV FILTERING, FREQUENCY-SELECTIVE FILTERING, AND POLYNOMIAL REGRESSION