Efficient estimation of a semiparametric partially linear varying coefficient model

• 成果类型: Article
• 署名作者: Ahmad, I; Leelahanon, S; Li, Q
• 署名单位: State University System of Florida; University of Central Florida; Thammasat University; Texas A&M University System; Texas A&M University College Station
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053604000000931
• 发表日期: 2005
• 页码: 258-283
• 关键词: Asymptotic Normality; local asymptotics; Cross-validation; regression

摘要

In this paper we propose a general series method to estimate a semiparametric partially linear varying coefficient model. We establish the consistency and root n-normality property of the estimator of the finite-dimensional parameters of the model, We further show that, when the error is conditionally homoskedastic. this estimator is semiparametrically efficient in the sense that the inverse of the asymptotic variance of the estimator of the finite-dimensional parameter reaches the semiparametric efficiency bound of this model. A small-scale simulation is reported to examine the finite.sample performance of the proposed estimator, and an empirical application is presented to illustrate the usefulness of the proposed method in practice. We also discuss how to obtain an efficient estimation result when the error is conditional heteroskedastic.