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

Abstract

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 maximum likelihood iteration and to provide final estimates when maximum likelihood 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.

Keywords:

• State–space models
• Subspace methods
• Kalman filter
• System identification
• Maximum-likelihood

Acknowledgements

The authors gratefully acknowledge financial support from Ministerio de Educación y Ciencia, ref. SEJ2005-07388.