Statistical Inference of Partially Linear Spatial Autoregressive Model Under Constraint Conditions

作     者：LI Tizheng CHENG Yaoyao LI Tizheng;CHENG Yaoyao

作者机构：School of ScienceXi’an University of Architecture and TechnologyXi’an710055China 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2023年第36卷第6期

页      面：2624-2660页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by the Natural Science Foundation of Shaanxi Province under Grant No.2021JM349 the Natural Science Foundation of China under Grant Nos.11972273 and 52170172 

主　　题：Constraint conditions partially linear spatial autoregressive model series estimation spatial correlation two-stage least squares 

摘      要：In many application fields of regression analysis,prior information about how explanatory variables affect response variable of interest is often available and can be formulated as constraints on regression *** this paper,the authors consider statistical inference of partially linear spatial autoregressive model under constraint *** combining series approximation method,twostage least squares method and Lagrange multiplier method,the authors obtain constrained estimators of the parameters and function in the partially linear spatial autoregressive model and investigate their asymptotic ***,the authors propose a testing method to check whether the parameters in the parametric component of the partially linear spatial autoregressive model satisfy linear constraint conditions,and derive asymptotic distributions of the resulting test statistic under both null and alternative *** results show that the proposed constrained estimators have better finite sample performance than the unconstrained estimators and the proposed testing method performs well in finite ***,a real example is provided to illustrate the application of the proposed estimation and testing methods.