Bootstrap of residual processes in regression: to smooth or not to smooth?

N Neumeyer and I Van Keilegom

Biometrika, 2019, vol. 106, issue 2, 385-400

Abstract: SummaryIn this paper we consider regression models with centred errors, independent of the covariates. Given independent and identically distributed data and given an estimator of the regression function, which can be parametric or nonparametric in nature, we estimate the distribution of the error term by the empirical distribution of estimated residuals. To approximate the distribution of this estimator, Koul & Lahiri (1994) and Neumeyer (2009) proposed bootstrap procedures based on smoothing the residuals before drawing bootstrap samples. So far it has been an open question as to whether a classical nonsmooth residual bootstrap is asymptotically valid in this context. Here we solve this open problem and show that the nonsmooth residual bootstrap is consistent. We illustrate the theoretical result by means of simulations, which demonstrate the accuracy of this bootstrap procedure for various models, testing procedures and sample sizes.

Keywords: Empirical distribution function; Kernel smoothing; Linear regression; Location model; Nonparametric regression (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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