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Testing for symmetric error distribution in nonparametric regression models

  • Neumeyer, Natalie
  • Dette, Holger
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    For the problem of testing symmetry of the error distribution in a nonparametric regression model we propose as a test statistic the difference between the two empirical distribution functions of estimated residuals and their counterparts with opposite signs. The weak convergence of the difference process to a Gaussian process is shown. The covariance structure of this process depends heavily on the density of the error distribution, and for this reason the performance of a symmetric wild bootstrap procedure is discussed in asymptotic theory and by means of a simulation study. In contrast to the available procedures the new test is also applicable under heteroscedasticity.

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    Paper provided by Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen in its series Technical Reports with number 2003,11.

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    Date of creation: 2003
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    Handle: RePEc:zbw:sfb475:200311
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    1. Qiwei Yao & Rob J. Hyndman, 2002. "Nonparametric estimation and symmetry tests for conditional density functions," LSE Research Online Documents on Economics 6092, London School of Economics and Political Science, LSE Library.
    2. Koziol, James A., 1985. "A note on testing symmetry with estimated parameters," Statistics & Probability Letters, Elsevier, vol. 3(4), pages 227-230, July.
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