Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models

• 成果类型: Article
• 署名作者: Cai, Zongwu; Xu, Xiaoping
• 署名单位: University of North Carolina; University of North Carolina Charlotte; Xiamen University; China University of Geosciences
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0102
• 发表日期: 2009
• 页码: 371-383
• 关键词: nonlinear time-series; REGRESSION QUANTILES; growth charts; conditional quantile; linear-models; inference; splines; selection

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.