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Kalman filtering as an alternative to Ordinary Least Squares — Some theoretical considerations and empirical results

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Abstract

The purpose of this paper is to highlight the superiority of the Kalman filter over Ordinary Least Squares for estimating the unknown coefficients of the classical linear regression model. Both methods are analyzed with respect to their optimality properties and their usefulness in dealing with multicollinearity. Theoretical results are applied to two economic models.

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  1. Faculty of Social Sciences, Department of Economics, The University of the West Indies, St. Augustine, Trinidad & Tobago, West Indies

    P. K. Watson

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  1. P. K. Watson
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Watson, P.K. Kalman filtering as an alternative to Ordinary Least Squares — Some theoretical considerations and empirical results. Empirical Economics 8, 71–85 (1983). https://doi.org/10.1007/BF01973191

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  • DOI: https://doi.org/10.1007/BF01973191

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