ABSTRACT
State-space models (SSMs) have been discussed in the literature for a number of decades. They are models that rely on a decomposition that separates the observational errors from the temporal evolution. The former usually consists of temporally independent specifications that handle the characteristics of the observational process. The latter is devised to describe the temporal dependence at a latent, unobserved level through evolution disturbances. In the most general form, the observational and evolution disturbances may be related, but in a typical set-up they are independent. SSMs were originally introduced for 166Gaussian, hence continuous, time series data, but the above decomposition made it easy to extend them to discrete-valued time series. This chapter describes SSMs with a view towards their use for such data.
