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Assessing white noise assumption with semi-parametric additive partial linear models

Author

Listed:
  • Tianyong Zhang

    (Chongqing Technology and Business University)

  • Demei Yuan

    (Chongqing Technology and Business University)

  • Jiali Ma

    (Chongqing Technology and Business University)

  • Xuemei Hu

    (Chongqing Technology and Business University
    Chinese Academy of Sciences)

Abstract

In this paper, we present two test statistics for assessing white noise assumption with semi-parametric additive partial linear models. The test statistics are shown to have asymptotic normal or chi-squared distributions under the null hypothesis that the model errors belong to white noise series. By applying R, Monte Carlo experiments are conducted to examine the finite sample performance of the test statistics. Simulation results indicate that the test statistics perform satisfactorily in both estimated sizes and powers.

Suggested Citation

  • Tianyong Zhang & Demei Yuan & Jiali Ma & Xuemei Hu, 2017. "Assessing white noise assumption with semi-parametric additive partial linear models," Statistical Papers, Springer, vol. 58(2), pages 417-431, June.
    • Handle: RePEc:spr:stpapr:v:58:y:2017:i:2:d:10.1007_s00362-015-0705-z
    DOI: 10.1007/s00362-015-0705-z
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    References listed on IDEAS

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    Cited by:

    1. Suli Cheng & Jianbao Chen, 2021. "Estimation of partially linear single-index spatial autoregressive model," Statistical Papers, Springer, vol. 62(1), pages 495-531, February.

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