Penalized Estimation Based Variable Selection for Semiparametric Regression Models with Endogenous Covariates

Abstract

In this paper, we study the variable selection problem for the parametric components of semiparametric regression models with endogenous variables. Based on the penalized empirical likelihood technology and the bias adjustment method, we propose a penalized empirical likelihood based variable selection procedure. Simulation studies show that the proposed variable selection procedure is workable, and the resulting estimator is consistent.