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The exact likelihood function of a vector autoregressive moving average process

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  • Gallego, Jose L.

Abstract

Available closed form expressions for the determinant and inverse of the covariance matrix of a set of observations generated by a vector autoregressive moving average model are derived following a unified and simplified approach. Computational guidelines to estimate these models by maximum likelihood or nonlinear least squares methods are also given.

Suggested Citation

  • Gallego, Jose L., 2009. "The exact likelihood function of a vector autoregressive moving average process," Statistics & Probability Letters, Elsevier, vol. 79(6), pages 711-714, March.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:6:p:711-714
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    Cited by:

    1. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    2. Chiranjit Dutta & Nalini Ravishanker & Sumanta Basu, 2022. "Modeling Multivariate Positive-Valued Time Series Using R-INLA," Papers 2206.05374, arXiv.org, revised Jul 2022.
    3. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
    4. Dufour, Jean-Marie & Jouini, Tarek, 2014. "Asymptotic distributions for quasi-efficient estimators in echelon VARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 69-86.

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