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Empirical likelihood for the smoothed LAD estimator in infinite variance autoregressive models

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Listed:
  • Li, Jinyu
  • Liang, Wei
  • He, Shuyuan
  • Wu, Xianbin

Abstract

This paper proposes an empirical likelihood method to estimate the parameters of infinite variance autoregressive (IVAR) models and to construct confidence regions for the parameters. Simulation studies suggest that in small sample case, the empirical likelihood confidence regions may be more accurate than the confidence regions constructed by the normal approximation based on the self-weighted LAD estimator proposed by Ling (2005).

Suggested Citation

  • Li, Jinyu & Liang, Wei & He, Shuyuan & Wu, Xianbin, 2010. "Empirical likelihood for the smoothed LAD estimator in infinite variance autoregressive models," Statistics & Probability Letters, Elsevier, vol. 80(17-18), pages 1420-1430, September.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:17-18:p:1420-1430
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    References listed on IDEAS

    as
    1. Davis, Richard A. & Knight, Keith & Liu, Jian, 1992. "M-estimation for autoregressions with infinite variance," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 145-180, February.
    2. Chan, Ngai Hang & Ling, Shiqing, 2006. "Empirical Likelihood For Garch Models," Econometric Theory, Cambridge University Press, vol. 22(3), pages 403-428, June.
    3. Shiqing Ling, 2005. "Self‐weighted least absolute deviation estimation for infinite variance autoregressive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 381-393, June.
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