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Asymptotics of M-estimators in non-linear regression with long memory designs

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  • Koul, Hira L.
  • Baillie, Richard T.

Abstract

This paper derives the asymptotic distribution of a class of M-estimators in a family of non-linear regression models when the errors and the design variables are long memory moving averages. The class of estimators includes analogs of the least square, least absolute deviation and the Huber(c) estimators. A simulation study comparing the finite sample behaviour of the least absolute deviation and the least-square estimators is also included.

Suggested Citation

  • Koul, Hira L. & Baillie, Richard T., 2003. "Asymptotics of M-estimators in non-linear regression with long memory designs," Statistics & Probability Letters, Elsevier, vol. 61(3), pages 237-252, February.
    • Handle: RePEc:eee:stapro:v:61:y:2003:i:3:p:237-252
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    References listed on IDEAS

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    1. Giraitis, Liudas & Koul, Hira L. & Surgailis, Donatas, 1996. "Asymptotic normality of regression estimators with long memory errors," Statistics & Probability Letters, Elsevier, vol. 29(4), pages 317-335, September.
    2. Koul, Hira L., 1992. "M-estimators in linear models with long range dependent errors," Statistics & Probability Letters, Elsevier, vol. 14(2), pages 153-164, May.
    3. Baillie, Richard T & Bollerslev, Tim, 1994. "The long memory of the forward premium," Journal of International Money and Finance, Elsevier, vol. 13(5), pages 565-571, October.
    4. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
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    Cited by:

    1. Beran, Jan & Weiershäuser, Arno, 2011. "On spline regression under Gaussian subordination with long memory," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 315-335, February.

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