Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/130022 
Year of Publication: 
2014
Series/Report no.: 
cemmap working paper No. CWP53/14
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This work proposes new inference methods for the estimation of a regression coefficient of interest in quantile regression models. We consider high-dimensional models where the number of regressors potentially exceeds the sample size but a subset of them suffice to construct a reasonable approximation of the unknown quantile regression function in the model. The proposed methods are protected against moderate model selection mistakes, which are often inevitable in the approximately sparse model considered here. The methods construct (implicitly or explicitly) an optimal instrument as a residual from a density-weighted projection of the regressor of interest on other regressors. Under regularity conditions, the proposed estimators of the quantile regression coefficient are asymptotically root-n normal, with variance equal to the semi-parametric efficiency bound of the partially linear quantile regression model. In addition, the performance of the technique is illustrated through Monte-carlo experiments and an empirical example, dealing with risk factors in childhood malnutrition. The numerical results confirm the theoretical findings that the proposed methods should outperform the naive post-model selection methods in non-parametric settings. Moreover, the empirical results demonstrate soundness of the proposed methods.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
854.87 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.