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On convergence of LAD estimates in autoregression with infinite variance

Author

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  • An, Hong-Zhi
  • Chen, Zhao-guo

Abstract

The least absolute deviation estimates L(N), from N data points, of the autoregressive constants a = (a1, ..., aq)' for a stationary autoregressive model, are shown to have the property that N[sigma](L(N) - a) converge to zero in probability, for [sigma]

Suggested Citation

  • An, Hong-Zhi & Chen, Zhao-guo, 1982. "On convergence of LAD estimates in autoregression with infinite variance," Journal of Multivariate Analysis, Elsevier, vol. 12(3), pages 335-345, September.
    • Handle: RePEc:eee:jmvana:v:12:y:1982:i:3:p:335-345
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    Cited by:

    1. Zhu, Qianqian & Zheng, Yao & Li, Guodong, 2018. "Linear double autoregression," Journal of Econometrics, Elsevier, vol. 207(1), pages 162-174.
    2. Xinghui Wang & Shuhe Hu, 2017. "Asymptotics of self-weighted M-estimators for autoregressive models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(1), pages 83-92, January.
    3. Zhao Chen & Runze Li & Yaohua Wu, 2012. "Weighted quantile regression for AR model with infinite variance errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 715-731.

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