Bootstrap confidence bands and partial linear quantile regression

Song Song, Ritov, Ya’acov and Wolfgang Härdle

Journal of Multivariate Analysis, 2012, vol. 107, issue C, 244-262

Abstract: In this paper bootstrap confidence bands are constructed for nonparametric quantile estimates of regression functions, where resampling is done from a suitably estimated empirical distribution function (edf) for residuals. It is known that the approximation error for the confidence band by the asymptotic Gumbel distribution is logarithmically slow. It is proved that the bootstrap approximation provides an improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. An economic application considers the labor market differential effect with respect to different education levels.

Keywords: Bootstrap; Quantile regression; Confidence bands; Nonparametric fitting; Kernel smoothing; Partial linear model (search for similar items in EconPapers)
JEL-codes: C14 C21 C31 J01 J31 J71 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (11)

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DOI: 10.1016/j.jmva.2012.01.020

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