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

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Author Info

  • Alfredo García Hiernaux

    ()
    (Universidad Pública de Navarra, Departamento de Fundamentos del Análisis Económico II.)

  • José Casals Carro

    ()
    (Universidad Complutense de Madrid, Dpto. de Fundamentos y Análisis Económico II)

  • Miguel Jerez

    ()
    (Universidad Complutense de Madrid, Dpto. de Fundamentos y Análisis Económico II)

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.

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Bibliographic Info

Paper 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.

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Length: 30 pages
Date of creation: 2005
Date of revision:
Handle: RePEc:ucm:doicae:0504

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Keywords: State-space models; subspace methods; Kalman Filter; system identification.;

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  1. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Wiley Blackwell, vol. 61(2), pages 247-64, April.
  2. D.S. Poskitt, . "Specification of echelon form VARMA models," Statistic und Oekonometrie 9305, Humboldt Universitaet Berlin.
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