Regularization Parameter Selection for Penalized Empirical Likelihood Estimator

Abstract

Penalized estimation is a useful technique for variable selection when the number of candidate variables is large. A crucial issue in penalized estimation is the selection of the regularization parameter because the performance of the estimator largely depends on an appropriate choice. However, no theoretically sound selection method currently exists for the penalized estimation of moment restriction models. To address this important issue, we develop a novel information criterion, which we call the empirical likelihood information criterion, to select the regularization parameter of the penalized empirical likelihood estimator. The information criterion is derived as an estimator of the expected value of the Kullback–Leibler information criterion from an estimated model to the true data generating process. We present a Monte Carlo simulation that demonstrates the efficacy of the proposed method.