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Abstract

In this paper the Kalman filter and regression approaches for estimating linear state space models are compared. It is argued that the Kalman filter is no more efficient from a computational point of view, is relatively more complex and hence more obtruse, and that as consequence its central role in the smoothing, estimation and prediction of time series is questionable.

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