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

Alfredo Garcia-Hiernaux, José Casals Carro () and Miguel Jerez
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José Casals Carro: Universidad Complutense de Madrid, Dpto. de Fundamentos y Análisis Económico II

Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico

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. (search for similar items in EconPapers)
Pages: 30 pages
Date: 2005
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets and nep-knm
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ucm:doicae:0504

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