Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/39310
Authors: 
Härdle, Wolfgang Karl
Ritov, Ya'acov
Song, Song
Year of Publication: 
2010
Series/Report no.: 
SFB 649 discussion paper 2010,002
Abstract: 
In this paper uniform confidence bands are constructed for nonparametric quantile estimates of regression functions. The method is based on the bootstrap, where resampling is done from a suitably estimated empirical density function (edf) for residuals. It is known that the approximation error for the uniform confidence band by the asymptotic Gumbel distribution is logarithmically slow. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. Comparison to classic asymptotic uniform bands is presented through a simulation study. An economic application considers the labour market differential effect with respect to different education levels.
Subjects: 
Bootstrap
Quantile Regression
Confidence Bands
Nonparametric Fitting
Kernel Smoothing
Partial Linear Model
JEL: 
C14
C21
C31
J01
J31
J71
Document Type: 
Working Paper

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