ENDOGENEITY IN HIGH DIMENSIONS

• 成果类型: Article
• 署名作者: Fan, Jianqing; Liao, Yuan
• 署名单位: Princeton University; University System of Maryland; University of Maryland College Park
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/13-AOS1202
• 发表日期: 2014
• 页码: 872-917
• 关键词: nonconcave penalized likelihood; moment selection procedures; adaptive elastic-net; variable selection; model selection; efficient estimation; generalized-method; linear-models; regression; Lasso

摘要

Most papers on high-dimensional statistics are based on the assumption that none of the regressors are correlated with the regression error, namely, they are exogenous. Yet, endogeneity can arise incidentally from a large pool of regressors in a high-dimensional regression. This causes the inconsistency of the penalized least-squares method and possible false scientific discoveries. A necessary condition for model selection consistency of a general class of penalized regression methods is given, which allows us to prove formally the inconsistency claim. To cope with the incidental endogeneity, we construct a novel penalized focused generalized method of moments (FGMM) criterion function. The FGMM effectively achieves the dimension reduction and applies the instrumental variable methods. We show that it possesses the oracle property even in the presence of endogenous predictors, and that the solution is also near global minimum under the over-identification assumption. Finally, we also show how the semi-parametric efficiency of estimation can be achieved via a two-step approach.