自回归模型
Lasso(编程语言)
估计员
特征选择
一致性(知识库)
选型
数学
对数
选择(遗传算法)
面板数据
甲骨文公司
半参数模型
计量经济学
数学优化
计算机科学
统计
人工智能
软件工程
数学分析
万维网
标识
DOI:10.1080/03610926.2022.2119088
摘要
This paper investigates variable selection in semiparametric spatial autoregressive panel data model with random effects. A penalized profile maximum-likelihood method is proposed with adaptive lasso penalty which achieves parameter estimation and variable selection at the same time. Under some regular conditions, we prove the theoretical properties of the estimators, including consistency and oracle property. In addition, we develop a feasible logarithm and carry out numerical simulations to examine the finite sample performance of this method. At last, a real data study about the investment influencing factors of the “Belt and Road” initiative is presented for illustration purpose.
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