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GR-estimates for an autoregressive time series

Author

Listed:
  • Terpstra, Jeffrey T.
  • McKean, Joseph W.
  • Naranjo, Joshua D.

Abstract

A weighted rank-based (GR) estimate for estimating the parameter vector of an autoregressive time series is considered. When the weights are constant, the estimate is equivalent to using Jaeckel's estimate with Wilcoxon scores. Asymptotic linearity properties are derived for the GR-estimate. Based on these properties, the GR-estimate is shown to be asymptotically normal at rate n1/2.

Suggested Citation

  • Terpstra, Jeffrey T. & McKean, Joseph W. & Naranjo, Joshua D., 2001. "GR-estimates for an autoregressive time series," Statistics & Probability Letters, Elsevier, vol. 51(2), pages 165-172, January.
    • Handle: RePEc:eee:stapro:v:51:y:2001:i:2:p:165-172
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    Citations

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    Cited by:

    1. Hallin, Marc & La Vecchia, Davide, 2017. "R-estimation in semiparametric dynamic location-scale models," Journal of Econometrics, Elsevier, vol. 196(2), pages 233-247.
    2. 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.
    3. Terpstra, Jeffrey T. & Rao, M. Bhaskara, 2002. "On the asymptotic distribution of a multivariate GR-estimate for a VAR(p) time series," Statistics & Probability Letters, Elsevier, vol. 60(2), pages 219-230, November.
    4. 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.

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