Structural time series models are models which are formulated directly in terms of components of interest. They have a considerable intuitive appeal, particularly for economic and social time series. Furthermore, they provide a clear link with regression models, both in their technical formulation and in the model selection methodology which they employ. The potential of such models is only now beginning to be realised, and it seems to be an appropriate time to write a book which provides a unified view of the area and points the direction towards future research.

The Kaiman filter plays a fundamental role in handling structural time series models. This technique was originally developed and exploited in control engineering. It has been increasingly used in areas such as economics, and a good deal of work has been done modifying it for use with small samples. Chapter 3 brings these methods together, and it can be read independently of the material on structural time series models. For those who are primarily interested in carrying out applied work with structural time series models, it should perhaps be stressed that the Kaiman filter is simply a statistical algorithm, and it is only necessary to understand what the filter does, rather than how it does it. The same is true of the frequency-domain methods which can be used to construct the likelihood function.