Series estimation for single-index models under constraints

Chaohua Dong, Jiti Gao, Bin Peng

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

In this paper, a semi-parametric single-index model is investigated. The link function is allowed to be unbounded and has unbounded support that answers a pending issue in the literature. Meanwhile, the link function is treated as a point in an infinitely many dimensional function space which enables us to derive the estimates for the index parameter and the link function simultaneously. This approach is different from the profile method commonly used in the literature. The estimator is derived from an optimisation with the constraint of identification condition for the index parameter, which addresses an important problem in the literature of single-index models. In addition, making use of a property of Hermite orthogonal polynomials, an explicit estimator for the index parameter is obtained. Asymptotic properties for the two estimators of the index parameter are established. Their efficiency is discussed in some special cases as well. The finite sample properties of the two estimates are demonstrated through an extensive Monte Carlo study and an empirical example.

Original languageEnglish
Pages (from-to)299-335
Number of pages37
JournalAustralian & New Zealand Journal of Statistics
Volume61
Issue number3
DOIs
Publication statusPublished - Sep 2019

Keywords

  • asymptotic theory
  • closed-form estimation
  • cross-sectional model
  • Hermite series expansion

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