Template-type: ReDIF-Paper 1.0
Author-Name: Alfredo García Hiernaux
Author-Email: agarciah@ccee.ucm.es
Author-Workplace-Name: Universidad Pública de Navarra, Departamento de Fundamentos del Análisis Económico II. 
Author-Name: José Casals Carro
Author-Email: jcasalsc@cajamadrid.es 
Author-Workplace-Name: Universidad Complutense de Madrid, Dpto. de Fundamentos y Análisis Económico II
Author-Name: Miguel Jerez
Author-Email: mjerez@ccee.ucm.es
Author-Workplace-Name: Universidad Complutense de Madrid, Dpto. de Fundamentos y Análisis Económico II
Title: Fast estimation methods for time series models in state-space form
Abstract: We propose two fast, stable and consistent methods to estimate time series
	models expressed in their equivalent state-space form. They are useful
	both, to obtain adequate initial conditions for a maximum-likelihood iteration,
	or to provide final estimates when maximum-likelihood is considered
	inadequate or costly. The state-space foundation of these procedures implies
	that they can estimate any linear fixed-coefficients model, such as ARIMA,
	VARMAX or structural time series models. The computational and finitesample
	performance of both methods is very good, as a simulation exercise shows.
Keywords: State-space models, subspace methods, Kalman Filter, system identification. 
Length: 30 pages 
Creation-Date: 2005
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae0504.txt
File-URL: https://eprints.ucm.es/id/eprint/7881/1/0504.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:0504