Fast estimation methods for time series models in state-space form
AbstractWe 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.
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Bibliographic InfoPaper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales in its series Documentos del Instituto Complutense de Análisis Económico with number 0504.
Length: 30 pages
Date of creation: 2005
Date of revision:
State-space models; subspace methods; Kalman Filter; system identification.;
This paper has been announced in the following NEP Reports:
- NEP-KNM-2006-09-16 (Knowledge Management & Knowledge Economy)
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