Please use this identifier to cite or link to this item:
|Title:||An integrated maximum score estimator for a generalized censored quantile regression model|
|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fixed censored case can be extended readily to the case with random censoring for which other existing approaches are no longer applicable.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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 8, 2018
WEB OF SCIENCETM
checked on Feb 7, 2018
checked on Mar 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.