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Statistical inference for semiparametric varying -coefficient spatial autoregressive models under restricted conditions
被引:3
|作者:
Luo, Guowang
[1
]
Wu, Mixia
[1
]
机构:
[1] Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
基金:
中国国家自然科学基金;
关键词:
Semiparametric spatial autoregression;
Restricted conditions;
B-spline basis function;
Instrumental variable;
IN-VARIABLES MODELS;
PANEL-DATA MODELS;
NONPARAMETRIC DENSITY;
REGRESSION;
GMM;
D O I:
10.1080/03610918.2019.1693595
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 article considers statistical inference for restricted semiparametric varying-coefficient spatial autoregressive(SVCSAR) models. We propose a restricted estimation method for parametric and nonparametric components, and a Lagrange-multiplier-type test for testing hypotheses on the parametric component restrictions of SVCSAR models. Under mild conditions, we obtain the asymptotic normality for the resulting estimator of the parametric vector and the optimal convergence rate for that of nonparametric functions. Simulation studies are carried out to investigate the finite sample performance of the proposed method. The method is exemplified with Boston housing price data.
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页码:2268 / 2286
页数:19
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