Efficient estimation of a semiparametric partially linear varying coefficient model
Ibrahim Ahmad, Sittisak Leelahanon, and Qi Li
Source: Ann. Statist. Volume 33, Number 1
(2005), 258-283.
Abstract
In this paper we propose a general series method to estimate a semiparametric partially linear varying coefficient model. We establish the consistency and -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.
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Primary Subjects:
62G08
Keywords: Series estimation method; partially linear; varying coefficient; asymptotic normality; semiparametric efficiency
Full-text: Open access
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Permanent link to this document: http://projecteuclid.org/euclid.aos/1112967706
Digital Object Identifier: doi:10.1214/009053604000000931
Zentralblatt MATH identifier: 02182563
Mathematical Reviews number (MathSciNet): MR2157803
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