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Wild bootstrap estimation in partially linear models with heteroscedasticity

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  • You, Jinhong
  • Chen, Gemai

Abstract

This paper uses the wild bootstrap technique in the estimation of a heteroscedastic partially linear regression model. We show that this approach provides reliable approximation to the asymptotic distribution of the semiparametric least-square estimators of the linear regression coefficients and consistent estimators of the asymptotic covariance matrices even when the error variances are unequal. In comparison, this robustness property is not shared by the bootstrap estimation proposed in Liang et al. (2000. Bootstrap approximation in a partially linear regression model. J. Statist. Plann. Inference, 91, 413-426).

Suggested Citation

  • You, Jinhong & Chen, Gemai, 2006. "Wild bootstrap estimation in partially linear models with heteroscedasticity," Statistics & Probability Letters, Elsevier, vol. 76(4), pages 340-348, February.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:4:p:340-348
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    References listed on IDEAS

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    1. Hamilton, Scott A. & Truong, Young K., 1997. "Local Linear Estimation in Partly Linear Models," Journal of Multivariate Analysis, Elsevier, vol. 60(1), pages 1-19, January.
    2. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
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    Cited by:

    1. Kline Patrick & Santos Andres, 2012. "A Score Based Approach to Wild Bootstrap Inference," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 23-41, August.

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