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On the Asymptotic Relative Efficiency of Gaussian and Least Squares Estimators for Vector ARMA Models

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  • Poskitt, D. S.
  • Salau, M. O.

Abstract

This paper is concerned with the asymptotic relative efficiency of the Gaussian and least squares estimators when employed to estimate the parameters of vector ARMA models presented in echelon canonical form. The relative efficiency is assessed via the variance-covariance matrices of the limiting normal distributions of the two estimators. Situations under which substantial loss or gain in efficiency could be exprected are discussed and illustrated with some numerical examples.

Suggested Citation

  • Poskitt, D. S. & Salau, M. O., 1994. "On the Asymptotic Relative Efficiency of Gaussian and Least Squares Estimators for Vector ARMA Models," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 294-317, November.
    • Handle: RePEc:eee:jmvana:v:51:y:1994:i:2:p:294-317
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    Cited by:

    1. D.S. Poskitt, 2004. "Some Results on the Identification and Estimation of Vector ARMAX Processes," Monash Econometrics and Business Statistics Working Papers 12/04, Monash University, Department of Econometrics and Business Statistics.
    2. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    3. D. S. Poskitt, 2005. "A Note on the Specification and Estimation of ARMAX Systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 157-183, March.
    4. D. S. Poskitt & M. O. Salau, 1995. "On The Relationship Between Generalized Least Squares And Gaussian Estimation Of Vector Arma Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(6), pages 617-645, November.

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