摘要
主要研究部分线性空间自回归模型的重要变量选择问题.基于样条逼近技术和正交投影方法将部分线性空间自回归模型转化为线性空间自回归模型,从而消除了非参数分量对变量选择的影响.另外,通过工具变量调整技术消除变量内生性对模型变量选择的影响,进而保证了所提出的变量选择方法的有效性和相合性.
The selection of important variables for a class of partially linear spatial autoregressive models is considered in this paper.Based on spline approximation technique and orthogonal projection method,the partially linear spatial autoregressive model is transformed into a linear spatial autoregressive model,thus eliminating the influence of nonparametric components on the proposed variable selection method.In addition,the instrumental variable adjustment technology is used to eliminate the influence of endogenous variables on the selection of important variables.Hence,the proposed variable selection method is consistent and more effective.
作者
甘浩耕
赵培信
GAN Haogeng;ZHAO Peixin(Mathematics and Statistics School of Chongqing Technology and Business University;Chongqing Key Laboratory of Social Economy and Applied Statistics,Chongqing 400067,China)
出处
《四川文理学院学报》
2021年第5期52-57,共6页
Sichuan University of Arts and Science Journal
基金
国家社会科学基金项目“高维内生数据的半参数统计建模及其在环境治理绩效测度中的应用研究”(18BTJ035)。
关键词
部分线性空间自回归模型
正交投影
变量选择
Partially linear spatial autoregressive model
orthogonal projection
variable selection
