Estimation for single-index and partially linear single-index integrated models
Abstract
Estimation mainly for two classes of popular models, single-index and partially linear single-index models, is studied in this paper. Such models feature nonstationarity. Orthogonal series expansion is used to approximate the unknown integrable link functions in the models and a profile approach is used to derive the estimators. The findings include the dual rate of convergence of the estimators for the single-index models and a trio of convergence rates for the partially linear single-index models. A new central limit theorem is established for a plug-in estimator of the unknown link function. Meanwhile, a considerable extension to a class of partially nonlinear single-index models is discussed in Section 4. Monte Carlo simulation verifies these theoretical results. An empirical study furnishes an application of the proposed estimation procedures in practice.
Cite
@article{arxiv.1601.06003,
title = {Estimation for single-index and partially linear single-index integrated models},
author = {Chaohua Dong and Jiti Gao and Dag Tjøstheim},
journal= {arXiv preprint arXiv:1601.06003},
year = {2016}
}
Comments
Published at http://dx.doi.org/10.1214/15-AOS1372 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)