Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models

被引:41
作者
Cai, Zongwu [1 ,2 ]
Xu, Xiaoping [3 ]
机构
[1] Univ N Carolina, Dept Math & Stat, Charlotte, NC 28223 USA
[2] Xiamen Univ, Wang Yanan Inst Studies Econ, Xiamen, Peoples R China
[3] China Univ Geosci, Dept Stat, Coll Econ & Management, Wuhan 430074, Peoples R China
基金
美国国家科学基金会;
关键词
Bandwidth selection; Boundary effect; Covariance estimation; Kernel smoothing methods; Nonlinear time series; Quantile regression; Value-at-risk; Varying coefficients; NONLINEAR TIME-SERIES; REGRESSION QUANTILES; GROWTH CHARTS; CONDITIONAL QUANTILE; LINEAR-MODELS; INFERENCE; SPLINES; SELECTION;
D O I
10.1198/jasa.2009.0102
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In this article, quatile regression in methods are suggested for a class of smooth coefficient time series models. We use both local polynomial and local constant fitting schemes to estimate the smooth coefficients in a quantile framework. We establish the asymptotic propel-ties of both the local polynomial and local constant estimators For alpha-mixing time series. Also, a bandwidth selector based on the nonparametric version of the Akaike information criterion is sugggested. together with a consistent estimate of the asymptotic covariance matrix. Furthermore, the asymptotic behaviors of the estimators at boundaries are examined. A comparison of the local polynomial quantile estimator with the local constant estimator is presented. A simulation study is carried Out to illustrate the performance of estimates. An empirical application of the model to real data further demonstrates the potential of the proposed modeling procedures.
引用
收藏
页码:371 / 383
页数:13
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