Open Access
Translator Disclaimer
February 2005 Efficient estimation of a semiparametric partially linear varying coefficient model
Ibrahim Ahmad, Sittisak Leelahanon, Qi Li
Ann. Statist. 33(1): 258-283 (February 2005). DOI: 10.1214/009053604000000931

Abstract

In this paper we propose a general series method to estimate a semiparametric partially linear varying coefficient model. We establish the consistency and $\sqrt{n}$-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.

Citation

Download Citation

Ibrahim Ahmad. Sittisak Leelahanon. Qi Li. "Efficient estimation of a semiparametric partially linear varying coefficient model." Ann. Statist. 33 (1) 258 - 283, February 2005. https://doi.org/10.1214/009053604000000931

Information

Published: February 2005
First available in Project Euclid: 8 April 2005

zbMATH: 1064.62043
MathSciNet: MR2157803
Digital Object Identifier: 10.1214/009053604000000931

Subjects:
Primary: 62G08

Keywords: asymptotic normality , partially linear , Semiparametric efficiency , Series estimation method , varying coefficient

Rights: Copyright © 2005 Institute of Mathematical Statistics

JOURNAL ARTICLE
26 PAGES


SHARE
Vol.33 • No. 1 • February 2005
Back to Top