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Computing and estimating information matrices of weak ARMA models

Author

Listed:
  • Y. Boubacar Mainassara

    (LMB - Laboratoire de Mathématiques de Besançon (UMR 6623) - CNRS - Centre National de la Recherche Scientifique - UFC - Université de Franche-Comté - UBFC - Université Bourgogne Franche-Comté [COMUE], UFC - Université de Franche-Comté - UBFC - Université Bourgogne Franche-Comté [COMUE])

  • M. Carbon
  • Christian Francq

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, IP Paris - Institut Polytechnique de Paris)

Abstract

Numerous time series admit weak autoregressive-moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent nor martingale differences. The statistical inference of this general class of models requires the estimation of generalized Fisher information matrices. Analytic expressions are given for these information matrices, and consistent estimators, at any point of the parameter space, are proposed. The theoretical results are illustrated by means of Monte Carlo experiments and by analyzing the dynamics of daily returns and squared daily returns of financial series.
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Suggested Citation

  • Y. Boubacar Mainassara & M. Carbon & Christian Francq, 2012. "Computing and estimating information matrices of weak ARMA models," Post-Print hal-05417544, HAL.
    • Handle: RePEc:hal:journl:hal-05417544
    DOI: 10.1016/j.csda.2011.07.006
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    Cited by:

    1. is not listed on IDEAS
    2. Yacouba Boubacar Maïnassara & Landy Rabehasaina, 2020. "Estimation of weak ARMA models with regime changes," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 1-52, April.
    3. Y. Boubacar Maïnassara & A. Ilmi Amir, 2024. "Portmanteau tests for periodic ARMA models with dependent errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(2), pages 164-188, March.
    4. Boubacar Maïnassara, Yacouba & Rabehasaina, Landy, 2025. "Estimation of subcritical Galton Watson processes with correlated immigration," Stochastic Processes and their Applications, Elsevier, vol. 184(C).
    5. Bao, Yong & Hua, Ying, 2014. "On the Fisher information matrix of a vector ARMA process," Economics Letters, Elsevier, vol. 123(1), pages 14-16.
    6. Min, Aleksey & Czado, Claudia, 2014. "SCOMDY models based on pair-copula constructions with application to exchange rates," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 523-535.
    7. Christian Francq & Jean-Michel Zakoïan, 2013. "Estimating the Marginal Law of a Time Series With Applications to Heavy-Tailed Distributions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 412-425, October.
    8. Yacouba Boubacar Maïnassara & Eugen Ursu, 2023. "Estimating weak periodic vector autoregressive time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 958-997, September.
    9. Yacouba Boubacar Maïnassara & Youssef Esstafa & Bruno Saussereau, 2021. "Estimating FARIMA models with uncorrelated but non-independent error terms," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 549-608, October.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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