Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jeconom.2009.09.020
Title: An integrated maximum score estimator for a generalized censored quantile regression model
Authors: Chen, S. 
Keywords: Dimension reduction
Quantile regression
Transformation models
Issue Date: 2010
Source: Chen, S. (2010). An integrated maximum score estimator for a generalized censored quantile regression model. Journal of Econometrics 155 (1) : 90-98. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jeconom.2009.09.020
Abstract: Quantile regression techniques have been widely used in empirical economics. In this paper, we consider the estimation of a generalized quantile regression model when data are subject to fixed or random censoring. Through a discretization technique, we transform the censored regression model into a sequence of binary choice models and further propose an integrated smoothed maximum score estimator by combining individual binary choice models, following the insights of Horowitz (1992) and Manski (1985). Unlike the estimators of Horowitz (1992) and Manski (1985), our estimators converge at the usual parametric rate through an integration process. In the case of fixed censoring, our approach overcomes a major drawback of existing approaches associated with the curse-of-dimensionality problem. Our approach for the fixed censored case can be extended readily to the case with random censoring for which other existing approaches are no longer applicable. Both of our estimators are consistent and asymptotically normal. A simulation study demonstrates that our estimators perform well in finite samples.©2009 Elsevier B.V. All rights reserved.
Source Title: Journal of Econometrics
URI: http://scholarbank.nus.edu.sg/handle/10635/19987
ISSN: 03044076
DOI: 10.1016/j.jeconom.2009.09.020
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