Quantile regression without the curse of unsmoothness

You-Gan Wang, Quanxi Shao and Min Zhu

Computational Statistics & Data Analysis, 2009, vol. 53, issue 10, 3696-3705

Abstract: We consider quantile regression models and investigate the induced smoothing method for obtaining the covariance matrix of the regression parameter estimates. We show that the difference between the smoothed and unsmoothed estimating functions in quantile regression is negligible. The detailed and simple computational algorithms for calculating the asymptotic covariance are provided. Intensive simulation studies indicate that the proposed method performs very well. We also illustrate the algorithm by analyzing the rainfall-runoff data from Murray Upland, Australia.

Date: 2009
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Citations: View citations in EconPapers (15)

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