Single-index composite quantile regression with heteroscedasticity and general error distributions

Rong Jiang (), Wei-Min Qian () and Zhan-Gong Zhou ()

Statistical Papers, 2016, vol. 57, issue 1, 185-203

Abstract: It is known that composite quantile regression (CQR) could be much more efficient and sometimes arbitrarily more efficient than the least squares estimator. Based on CQR method, we propose a weighted CQR (WCQR) method for single-index models with heteroscedasticity and general error distributions. Because of the use of weights, the estimation bias is eliminated asymptotically. By comparing asymptotic relative efficiency, WCQR estimation outperforms the CQR estimation and least squares estimation. The simulation studies and a real data application are conducted to illustrate the finite sample performance of the proposed methods. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Composite quantile regression; Asymptotic efficiency; Single-index model (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00362-014-0646-y

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