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Bootstrap tests for the error distribution in linear and nonparametric regression models

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  • Nagel, Eva-Renate
  • Dette, Holger
  • Neumeyer, Natalie
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    Abstract

    In this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and nonparametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution-free and the parametric bootstrap is applied to deal with this problem. The performance of the resulting bootstrap test is investigated from an asymptotic point of view and by means of a simulation study. The results demonstrate that even for moderate sample sizes the parametric bootstrap provides a reliable and easy accessible solution to the problem of goodness-of-fit testing of assumptions regarding the error distribution in linear and nonparametric regression models. --

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    File URL: http://econstor.eu/bitstream/10419/22550/1/tr38-04.pdf
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    Bibliographic Info

    Paper provided by Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen in its series Technical Reports with number 2004,38.

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    Date of creation: 2004
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    Handle: RePEc:zbw:sfb475:200438

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    Related research

    Keywords: goodness-of-fit; residual process; parametric bootstrap; linear model; analysis of variance; M-estimation; nonparametric regression;

    References

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    1. Efromovich, Sam & Samarov, Alex, 1996. "Asymptotic equivalence of nonparametric regression and white noise model has its limits," Statistics & Probability Letters, Elsevier, vol. 28(2), pages 143-145, June.
    2. repec:cup:cbooks:9780521496032 is not listed on IDEAS
    3. Koul, H. L. & Lahiri, S. N., 1994. "On Bootstrapping M-Estimated Residual Processes in Multiple Linear-Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 49(2), pages 255-265, May.
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    Cited by:
    1. Einmahl, J.H.J. & Keilegom, I. van, 2006. "Tests for Independence in Nonparametric Regression," Discussion Paper 2006-80, Tilburg University, Center for Economic Research.
    2. J. Baixauli & Susana Alvarez, 2006. "Evaluating effects of excess kurtosis on VaR estimates: Evidence for international stock indices," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 27-46, August.

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