2018-03-052018-03-052018-01Rivas Martínez, G.I. y Jiménez Gamero, M.D. (2018). Computationally efficient goodness-of-fit tests for the error distribution in nonparametric regression. Revstat Statistical Journal, 16 (1), 137-166.1645-6726https://hdl.handle.net/11441/70740Several procedures have been proposed for testing goodness-of-fit to the error distribution in nonparametric regression models. The null distribution of the associated test statistics is usually approximated by means of a parametric bootstrap which, under certain conditions, provides a consistent estimator. This paper considers a goodness-of-fit test whose test statistic is an L2 norm of the difference between the empirical characteristic function of the residuals and a parametric estimate of the characteristic function in the null hypothesis. It is proposed to approximate the null distribution through a weighted bootstrap which also produces a consistent estimator of the null distribution but, from a computational point of view, is more efficient than the parametric bootstrap.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Goodness-of-fitEmpirical characteristic functionRegression residualsWeighted bootstrapConsistencyinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess