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Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models

id：2094 时间：20131014 status：published 点击数：
杂志Journal of the American Statis   December 2008, Vol. 103, No. 484
作者Zongwu Cai, Xiaoping Xu
正文We suggest quantile regression methods 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 properties of both the local polynomial and local constant estimators for α-mixing time series. We also suggest a bandwidth selector based on the nonparametric version of the Akaike information criterion, along with a consistent estimate of the asymptotic covariance matrix.We evaluate the asymptotic behaviors of the estimators at boundaries and compare the local polynomial quantile estimator and the local constant estimator. 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.
JEL-Codes：
关键词：Bandwidth selection; Boundary effect; Covariance estimation; Kernel smoothing method; Nonlinear time series; Quantile regression; Value-at-risk; Varying coefficients.
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