√n-uniformly consistent density estimation in nonparametric regression models

被引:19
|
作者
Escanciano, Juan Carlos [2 ]
Jacho-Chavez, David T. [1 ]
机构
[1] Emory Univ, Dept Econ, Atlanta, GA 30322 USA
[2] Indiana Univ, Dept Econ, Bloomington, IN 47405 USA
关键词
Density estimation; Kernel smoothing; U-processes; SEMIPARAMETRIC ESTIMATION; MARGINAL DENSITY; CONVERGENCE; CHOICE; RATES;
D O I
10.1016/j.jeconom.2011.09.017
中图分类号
F [经济];
学科分类号
02 ;
摘要
The paper introduces a root n-consistent estimator of the probability density function of the response variable in a nonparametric regression model. The proposed estimator is shown to have a (uniform) asymptotic normal distribution, and it is computationally very simple to calculate. A Monte Carlo experiment confirms our theoretical results. The results derived in the paper adapt general U-processes theory to the inclusion of infinite dimensional nuisance parameters. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:305 / 316
页数:12
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