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

Author

Listed:
  • 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.

Suggested Citation

  • Alfredo García Hiernaux & José Casals Carro & Miguel Jerez, 2005. "Fast estimation methods for time series models in state-space form," Documentos de Trabajo del ICAE 0504, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    • Handle: RePEc:ucm:doicae:0504
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    References listed on IDEAS

    as
    1. Sergio Koreisha & Tarmo Pukkila, 1989. "Fast Linear Estimation Methods For Vector Autoregressive Moving‐Average Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(4), pages 325-339, July.
    2. Francq, Christian & Zakoïan, Jean-Michel, 2000. "Estimating Weak Garch Representations," Econometric Theory, Cambridge University Press, vol. 16(5), pages 692-728, October.
    3. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(2), pages 247-264.
    4. Sergio Koreisha & Tarmo Pukkila, 1990. "A Generalized Least‐Squares Approach For Estimation Of Autoregressive Moving‐Average Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 139-151, March.
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    1. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.

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    More about this item

    Keywords

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