Our starting place is the first order seasonal autoregressive model. Its series are shown to have canonical model-based decompositions whose finite-sample estimates, filters, and error covariances have simple revealing formulas from basic linear regression.We obtain analogous formulas for...

We describe observation driven time series models for Student-t and EGB2 conditional distributions in which the signal is a linear function of past values of the score of the conditional distribution. These specifications produce models that are easy to implement and deal with outliers by what...

The aim of this paper is to set out criteria for defining trend and seasonal components in a time series. The criteria are set up primarily in terms of properties involving prediction. Because a structural time series model is set up in terms of components of interest, the relevant information...