文献详情
Regularization methods for high-dimensional sparse control function models
文献类型：期刊
通讯作者：Zhang, JX (reprint author), Renmin Univ China, Sch Stat, Ctr Appl Stat, Beijing 100872, Peoples R China.
期刊名称：JOURNAL OF STATISTICAL PLANNING AND INFERENCE影响因子和分区
年：2020
卷：206
页码：111-126
ISSN：0378-3758
关键词：Endogeneity; Regularization; Control function; Consistency of estimation; Model selection; Penalized least squares
所属部门：统计学院
摘要：Traditional penalty-based methods might not achieve variable selection consistency when endogeneity exists in high-dimensional data. In this article we construct a regularization framework based on the two-stage control function model, so called the regularized control function (RCF) method, to estimate important covariate effects, select key instruments, and replace the CF-based hypothesis test with variable selection to identify truly endogenous predictors. Under appropriate conditions, we est ...More
Traditional penalty-based methods might not achieve variable selection consistency when endogeneity exists in high-dimensional data. In this article we construct a regularization framework based on the two-stage control function model, so called the regularized control function (RCF) method, to estimate important covariate effects, select key instruments, and replace the CF-based hypothesis test with variable selection to identify truly endogenous predictors. Under appropriate conditions, we establish theoretical properties of the RCF estimators, including the consistency of coefficient estimation and model selection. Simulation results confirm that the RCF method is effective and superior to its main competitor, the penalized least squares (PLS) method. The proposed method also provides insightful and interpretable results on a real data analysis. (C) 2019 Elsevier B.V. All rights reserved. ...Hide

DOI：10.1016/j.jspi.2019.09.007
百度学术：Regularization methods for high-dimensional sparse control function models
语言：外文
人气指数：3
浏览次数：3
基金：MOE Project of Key Research Institute of Humanities and Social Sciences at Universities [16JJD910002]; Outstanding Innovative Talents Cultivation Funded Programs 2018 of Renmin University of China
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