Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model

被引：5

作者：

Luo Guowang ^{[1
]}

Wu Mixia ^{[2
]}

Pang Zhen ^{[3
]}

机构：

[1] Guizhou Univ Finance & Econ, Coll Big Data Stat, Guiyang 550025, Peoples R China

[2] Beijing Univ Technol, Coll Stat & Data Sci, Fac Sci, Beijing 100124, Peoples R China

[3] Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Peoples R China

来源：

JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY | 2021年 / 34卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Confidence regions; empirical likelihood; instrumental variable; residual-adjusted; Wilks theorem; PARTIALLY LINEAR-MODEL; PANEL-DATA MODELS; STATISTICAL-INFERENCE; REGRESSION-ANALYSIS; CONFIDENCE-REGIONS; MISSING RESPONSE; GMM;

D O I：

10.1007/s11424-021-1088-y

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper empirical likelihood (EL)-based inference for a semiparametric varying-coefficient spatial autoregressive model is investigated. The maximum EL estimators for the parametric component and the nonparametric component are established. Furthermore, asymptotic properties of the proposed estimators and EL ratios are derived, and the corresponding confidence regions/bands are constructed. Their finite sample performances are studied via simulation and an example.

引用

页码：2310 / 2333

页数：24

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