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Robust estimation of nonlinear regression with autoregressive errors

Author

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  • Sinha, Sanjoy K.
  • Field, Christopher A.
  • Smith, Bruce

Abstract

Generalized M (or GM) estimation has been extended to the case of a nonlinear regression model with autoregressive and heteroscedastic errors. The robustness properties of the GM estimators have been investigated based on the time-series analog of Hampel's influence function. The asymptotic properties of these estimators have been studied in some detail.

Suggested Citation

  • Sinha, Sanjoy K. & Field, Christopher A. & Smith, Bruce, 2003. "Robust estimation of nonlinear regression with autoregressive errors," Statistics & Probability Letters, Elsevier, vol. 63(1), pages 49-59, May.
    • Handle: RePEc:eee:stapro:v:63:y:2003:i:1:p:49-59
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    References listed on IDEAS

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    1. Domowitz, Ian & White, Halbert, 1982. "Misspecified models with dependent observations," Journal of Econometrics, Elsevier, vol. 20(1), pages 35-58, October.
    2. Koul, Hira L. & Zhu, Zhiwei, 1995. "Bahadur-Kiefer representations for GM-estimators in autoregression models," Stochastic Processes and their Applications, Elsevier, vol. 57(1), pages 167-189, May.
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    Cited by:

    1. Tao Wang, 2024. "Nonlinear kernel mode‐based regression for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(2), pages 189-213, March.
    2. Bravo, Francesco & Li, Degui & Tjøstheim, Dag, 2021. "Robust nonlinear regression estimation in null recurrent time series," Journal of Econometrics, Elsevier, vol. 224(2), pages 416-438.
    3. Bin Yang & Min Chen & Tong Su & Jianjun Zhou, 2023. "Robust Estimation for Semi-Functional Linear Model with Autoregressive Errors," Mathematics, MDPI, vol. 11(2), pages 1-14, January.

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