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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

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    1. Casals, Jose & Sotoca, Sonia & Jerez, Miguel, 1999. "A fast and stable method to compute the likelihood of time invariant state-space models," Economics Letters, Elsevier, vol. 65(3), pages 329-337, December.
    2. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Oxford University Press, vol. 61(2), pages 247-264.
    3. Lutkepohl, Helmut & Poskitt, D S, 1996. "Specification of Echelon-Form VARMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 69-79, January.
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    Cited by:

    1. Alfredo García‐Hiernaux, 2011. "Forecasting linear dynamical systems using subspace methods," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(5), pages 462-468, September.
    2. Jiménez-Martín, Juan-Ángel & Cinca, Alfonso Novales, 2010. "State-uncertainty preferences and the risk premium in the exchange rate market," Economic Modelling, Elsevier, vol. 27(5), pages 1043-1053, September.

    More about this item

    Keywords

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

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