Estimation of semiparametric varying-coefficient spatial autoregressive models with missing in the dependent variable

被引：0

作者：

Luo, Guowang ^{[1
]}

Wu, Mixia ^{[1
]}

Pang, Zhen ^{[2
]}

机构：

[1] Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

[2] Hong Kong Polytech Univ, Dept Appl Math, Hung Hom, Kowloon, Hong Kong, Peoples R China

来源：

JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2020年 / 49卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Semi-parametric model; Spatial data; Propensity score; Missing data; Instrumental variable; PANEL-DATA MODELS; REGRESSION; INFERENCE; SPLINE; GMM;

D O I：

10.1007/s42952-019-00048-2

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper investigates estimation of semiparametric varying-coefficient spatial autoregressive models in which the dependent variable is missing at random. An inverse propensity score weighted sieve two-stage least squares (S-2SLS) estimation with imputation is proposed. The proposed estimators are shown to be consistent, no matter the initial value is taken as the naive S-2SLS estimate or the naive nonlinear least squares estimate, and the asymptotic distribution of the latter is also derived. Simulation studies are carried out to investigate the performance of the proposed estimator. The method is finally exemplified with one real data set on Boston housing prices.

引用

页码：1148 / 1172

页数：25

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