Fast estimation methods for time series models in state-space form

We propose two new, fast and stable methods to estimate time series models written in their equivalent state-space form. They are useful both, to obtain adequate initial conditions for a maximumlikelihood iteration, or to provide final estimates when maximumlikelihood is considered inadequate or computationally expensive. The state-space foundation of these procedures provides flexibility, as they can be applied to any linear fixed-coefficients model, such as ARIMA, VARMAX or structural time series models. A simulation exercise shows that their computational costs and finite-sample performance are very good.