The discrete Kalman filter applied to linear regression models: statistical considerations and an application |
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Authors: | Pieter W Otter |
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Institution: | *Econometric Institute, P.O. Box 800, Groningen. |
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Abstract: | In this paper we show how the Kalman filter, which is a recursive estimation procedure, can be applied to the standard linear regression model. The resulting "Kalman estimator" is compared with the classical least-squares estimator. The applicability and (dis)advantages of the filter are illustrated by means of a case study which consists of two parts. In the first part we apply the filter to a regression model with constant parameters and in the second part the filter is applied to a regression model with time-varying stochastic parameters. The prediction-powers of various "Kalman predictors" are compared with "least-squares predictors" by using T heil 's prediction-error coefficient U. |
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