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On-line estimation of ARMA models using Fisher-scoring

Author

Listed:
  • Abdelhamid Ouakasse
  • Guy Melard

Abstract

Recursive estimation methods for time series models usually make use of recurrences for the vector of parameters, the modelerror and its derivatives with respect to the parameters, plus a recurrence for the Hessian of the model error. An alternativemethod is proposed in the case of an autoregressive-moving average model, where the Hessian is not updated but is replaced,at each time, by the inverse of the Fisher information matrix evaluated at the current parameter. The asymptotic properties,consistency and asymptotic normality, of the new estimator are obtained. Monte Carlo experiments indicate that the estimatesmay converge faster to the true values of the parameters than when the Hessian is updated. The paper is illustrated by anexample on forecasting the speed of wind.

Suggested Citation

  • Abdelhamid Ouakasse & Guy Melard, 2014. "On-line estimation of ARMA models using Fisher-scoring," ULB Institutional Repository 2013/13844, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ulb:ulbeco:2013/13844
    Note: SCOPUS: ar.j
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    References listed on IDEAS

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    1. André Klein & Guy Melard, 1989. "On algorithms for computing the covariance matrix of estimates in autoregresive-moving average processes," ULB Institutional Repository 2013/13710, ULB -- Universite Libre de Bruxelles.
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    Cited by:

    1. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.

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    More about this item

    Keywords

    ARMA processes; Fisher information matrix; Recursive estimation; Time series;
    All these keywords.

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