"In 1990 Kalaba/Tesfatsion developed a Flexible Least Square (FLS) approach for estimating state space models as an alternative to Kalman filtering. In this paper we ask whether FLS is really an alternative. For answering this, we use a simulation using FLS as a regression model with time varying parameters. We will estimate this model with Kalman filtering and two variations of FLS. In a second step we will misspecify the so-called hyperstructure of the model and we will prove how the two ways of estimating (Kalman filtering and FLS) react to this misspecification."
Important Note: The findings of this paper have been misreported in several subsequent studies. What, in fact, Kladroba concludes (p. 10) is that "(FLS) is a method which achieves the same or better estimation results like Kalman Filtering and which does not cause more problems than Kalman Filtering." For a detailed discussion focusing on distinctions and commonalities between Kalman filtering and FLS, see
Kalaba and Tesfatsion (1990, Section 4)