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Rank‐based estimation for autoregressive moving average time series models

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  • Beth Andrews

Abstract

. We establish asymptotic normality and consistency for rank‐based estimators of autoregressive‐moving average model parameters. The estimators are obtained by minimizing a rank‐based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449–1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation.

Suggested Citation

  • Beth Andrews, 2008. "Rank‐based estimation for autoregressive moving average time series models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(1), pages 51-73, January.
    • Handle: RePEc:bla:jtsera:v:29:y:2008:i:1:p:51-73
    DOI: 10.1111/j.1467-9892.2007.00545.x
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    Cited by:

    1. M. Hallin & D. La Vecchia & H. Liu, 2022. "Center-Outward R-Estimation for Semiparametric VARMA Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 925-938, April.
    2. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    3. Marc Hallin & Davide La Vecchia, 2014. "Semiparametrically Efficient R-Estimation for Dynamic Location-Scale Models," Working Papers ECARES ECARES 2014-45, ULB -- Universite Libre de Bruxelles.
    4. Hallin, Marc & La Vecchia, Davide, 2017. "R-estimation in semiparametric dynamic location-scale models," Journal of Econometrics, Elsevier, vol. 196(2), pages 233-247.
    5. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Karl B. Gregory & Soumendra N. Lahiri & Daniel J. Nordman, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 442-461, May.

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