Heteroscedasticity checks for regression models

For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametri c regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in es timation of the nonparametric regression function. The limiting null distri bution of the test statistic remains the same in a wide range of the smooth ing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is sho wn that a variant of the wild bootstrap is consistent while the classical b ootstrap is not in the general case, but is applicable if some extra assump tion on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare wi th tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.