Dette, HolgerVan Keilegom, Ingrid2005-10-112005-10-112005-10-11http://hdl.handle.net/2003/2163610.17877/DE290R-14489In the common nonparametric regression model the problem of testing for the parametric form of the conditional variance is considered. A stochastic process based on the difference between the empirical processes obtained from the standardized nonparametric residuals under the null hypothesis (of a specific parametric form of the variance function) and the alternative is introduced and its weak convergence established. This result is used for the construction of a Cramér von Mises type statistic for testing the parametric form of the conditional variance. The finite sample properties of a bootstrap version of this test are investigated by means of a simulation study. In particular the new procedure is compared with some of the currently available methods for this problem and its performance is illustrated by means of a data example.enBootstrapKernel estimationNonparametric regressionResidual distributionTesting heteroscedasticityTesting homoscedasticity004report