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- A Nonparametric Test For Serial Independence Of Regression Errors

Author

Listed:
  • Juan Mora

    (Universidad de Alicante)

  • Miguel A. Delgado

    (Universidad Carlos III de Madrid)

Abstract

A test for serial independence of regression errors is proposed that is consistent in the direction ofserial dependence alternatives of first order. The test statistic is a function of aHoeffding-Blum-Kiefer-Rosenblatt type of empirical process, based on residuals. The resultantstatistic converges, surprisingly, to the same limiting distribution as the corresponding statisticbased on true errors.

Suggested Citation

  • Juan Mora & Miguel A. Delgado, 1999. "- A Nonparametric Test For Serial Independence Of Regression Errors," Working Papers. Serie AD 1999-28, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    • Handle: RePEc:ivi:wpasad:1999-28
    as

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    File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-1999-28.pdf
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    References listed on IDEAS

    as
    1. Csörgo, Miklós, 1979. "Strong approximations of the Hoeffding, Blum, Kiefer, Rosenblatt multivariate empirical process," Journal of Multivariate Analysis, Elsevier, vol. 9(1), pages 84-100, March.
    2. Yongmiao Hong, 1998. "Testing for pairwise serial independence via the empirical distribution function," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 429-453.
    3. Miguel A. Delgado, 1996. "Testing Serial Independence Using The Sample Distribution Function," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(3), pages 271-285, May.
    Full references (including those not matched with items on IDEAS)

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