超高维数据下半参数工具变量模型的双惩罚正则估计
Double Penalized Regularization Estimation for Semiparametric Instrumental Variable Models with Ultrahigh Dimensional Data
DOI: 10.12677/AAM.2022.118578, PDF,    国家社会科学基金支持
作者: 赵培信*：重庆工商大学数学与统计学院，重庆 ；唐新蓉：重庆工商大学资产管理处，重庆
关键词: 超高维数据；工具变量；惩罚估计；半参数模型；内生性变量；Ultra-High Dimensional Data； Instrumental Variable； Penalty Estimation； Semiparametric Model； Endogenous Variables
摘要: 在超高维数据下，考虑一类含内生性协变量的半参数工具变量模型的估计。结合惩罚估计技术以及SIS特征筛选方法，提出了一种识别有效工具变量的双惩罚正则估计方法，并得到了模型参数分量和非参数分量的工具变量调整估计。在一些正则条件下，证明了参数分量和非参数分量的估计均是相合的。最后通过一些数值仿真模拟研究了所提方法的有限样本性质。
Abstract: The estimation of a semiparametric instrumental variable model with endogenous covariates is considered under ultra-high dimensional data. Combined with penalty estimation technology and SIS feature screening method, a double penalized regularization estimation method for identifying optimal instrumental variables is proposed, and an optimal instrumental variable adjusted esti-mator of model parameter and nonparametric components are obtained. Under some regular con-ditions, it is proved that the estimators of parametric and nonparametric components are con-sistent. Finally, the finite sample properties of the proposed method are studied by some numerical simulations.
文章引用：赵培信, 唐新蓉. 超高维数据下半参数工具变量模型的双惩罚正则估计[J]. 应用数学进展, 2022, 11(8): 5484-5497. https://doi.org/10.12677/AAM.2022.118578

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