Orthogonality-projection-based estimation for semi-varying coefficient models with heteroscedastic errors

Yan-Yong Zhao, Jin-Guan Lin, Pei-Rong Xu and Xu-Guo Ye

Computational Statistics & Data Analysis, 2015, vol. 89, issue C, 204-221

Abstract: This paper is concerned with the estimation in semi-varying coefficient models with heteroscedastic errors. An iterated two-stage orthogonality-projection-based estimation is proposed. This method can easily be used to estimate the model parametric and nonparametric parts, as well as the variance function, and in the estimators the parametric part and nonparametric part do not affect each other. Under some mild conditions, the consistency, conditional biases, conditional variances and asymptotic normality of the resulting estimators are studied explicitly. Moreover, some simulation studies are carried out to examine the finite sample performance of the proposed methods. Finally, the methodologies are illustrated by a real data set.

Keywords: Asymptotic normality; Heteroscedastic errors; Orthogonality-projection; Semi-varying coefficient models (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:89:y:2015:i:c:p:204-221

DOI: 10.1016/j.csda.2015.03.018

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