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.