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Computation of the Fisher information matrix for time series models

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  • André Klein
  • Guy Melard

Abstract

The Fisher information matrix is useful in time series modeling mainly because the significance of estimated parameters can also be derived from it. It can also be used in iterative procedures of parameter estimation. The paper is mainly concerned with algorithmic aspects related to the computation of that matrix either asymptotically or exactly. After a review of the literature on the subject, several recent methods are described and compared from the point of view of (a) complexity, (b) accuracy, and (c) the class of models for which they can be used.

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  • André Klein & Guy Melard, 1995. "Computation of the Fisher information matrix for time series models," ULB Institutional Repository 2013/13736, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ulb:ulbeco:2013/13736
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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.
    2. André Klein & Guy Melard, 1994. "Computation of the Fisher information matrix for SISO models," ULB Institutional Repository 2013/13728, ULB -- Universite Libre de Bruxelles.
    3. André Klein & Guy Melard, 1994. "The information matrix of multiple input single output time series models," ULB Institutional Repository 2013/13732, ULB -- Universite Libre de Bruxelles.
    4. Newton, H. Joseph, 1978. "The information matrices of the parameters of multiple mixed time series," Journal of Multivariate Analysis, Elsevier, vol. 8(2), pages 317-323, June.
    5. André Klein & Guy Melard, 1990. "Fisher's information matrix for seasonal autoregressive-moving average models," ULB Institutional Repository 2013/13718, ULB -- Universite Libre de Bruxelles.
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    Cited by:

    1. Bao, Yong & Hua, Ying, 2014. "On the Fisher information matrix of a vector ARMA process," Economics Letters, Elsevier, vol. 123(1), pages 14-16.

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