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

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Hiernaux, Alfredo G. and Casals Carro, José and Jerez Méndez, Miguel (2005) Fast estimation methods for time series models in state-space form. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 04, 2005, ]

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Official URL: http://eprints.ucm.es/7881/


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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.


Item Type:Working Paper or Technical Report
Uncontrolled Keywords:State-space models, Subspace methods, Kalman Filter, System identification
Subjects:Social sciences > Economics > Econometrics
Series Name:Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
Volume:2005
Number:04
ID Code:7881
Deposited On:05 May 2008
Last Modified:04 Dec 2017 09:05

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