Modified see variable selection for linear instrumental variable regression models

Peixin Zhao and Liugen Xue

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 14, 4852-4861

Abstract: This article considers the problem of variable selection for a class of linear regression models with instrumental variables. We focus on the case that the covariates are endogenous variables, and some auxiliary instrumental variables are available. An instrumental variable based variable selection procedure is proposed by using a modified smooth-threshold estimating equations (SEE). The proposed method can attenuate the effect of endogeneity of covariates, and can avoid the convex optimization problem. Hence, it is flexible and easy to implement. Simulation results indicate that the proposed variable selection method is workable.

Date: 2023
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DOI: 10.1080/03610926.2013.777739

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