SFB 303 Discussion Paper No. A - 247

Author: Cao-Abad, R.
Title: Rates of Convergence for the Normal and the Bootstrap Approximations in Nonparametric Regression
Abstract: This paper is concerned with the distributions used to construct confidence intervals for the regression function in a nonparametric setup. The rates of convergence for the normal limit, its plug in approach and the wild bootstrap are compared conditionally on the explanatory variable X and also unconditionally. It turns out that the wild bootstrap performs better than the other approximations conditionally, but this behavior does not hold in the unconditional situation.
Keywords: Bootstrap, Kernel Smoothing, Nonparametric Regression
JEL-Classification-Number:
Creation-Date: June 1989
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