Please use this identifier to cite or link to this item:
|Title:||A single-index quantile regression model and its estimation|
|Citation:||Kong, E., Xia, Y. (2012-08). A single-index quantile regression model and its estimation. Econometric Theory 28 (4) : 730-768. ScholarBank@NUS Repository. https://doi.org/10.1017/S0266466611000788|
|Abstract:||Models with single-index structures are among the many existing popular semiparametric approaches for either the conditional mean or the conditional variance. This paper focuses on a single-index model for the conditional quantile. We propose an adaptive estimation procedure and an iterative algorithm which, under mild regularity conditions, is proved to converge with probability 1. The resulted estimator of the single-index parametric vector is root-n consistent, asymptotically normal, and based on simulation study, is more efficient than the average derivative method in Chaudhuri, Doksum, and Samarov (1997, Annals of Statistics 19, 760-777). The estimator of the link function converges at the usual rate for nonparametric estimation of a univariate function. As an empirical study, we apply the single-index quantile regression model to Boston housing data. By considering different levels of quantile, we explore how the covariates, of either social or environmental nature, could have different effects on individuals targeting the low, the median, and the high end of the housing market. © Copyright Cambridge University Press 2012.|
|Source Title:||Econometric Theory|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 5, 2018
WEB OF SCIENCETM
checked on Nov 27, 2018
checked on Nov 23, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.