Regularization Parameter Selection for Penalized Empirical Likelihood Estimator

28 Pages Posted: 23 May 2018

See all articles by Tomohiro Ando

Tomohiro Ando

University of Melbourne - Melbourne Business School

Naoya Sueishi

Kobe University - Graduate School of Economics

Date Written: May 11, 2018

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 KullbackÔÇôLeibler 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.

Keywords: Information Criterion, Series Estimation, Sparse Estimation

JEL Classification: C52

Suggested Citation

Ando, Tomohiro and Sueishi, Naoya, Regularization Parameter Selection for Penalized Empirical Likelihood Estimator (May 11, 2018). Available at SSRN: https://ssrn.com/abstract=3177880 or http://dx.doi.org/10.2139/ssrn.3177880

Tomohiro Ando

University of Melbourne - Melbourne Business School ( email )

200 Leicester Street
Carlton, Victoria 3053 3186
Australia

Naoya Sueishi (Contact Author)

Kobe University - Graduate School of Economics ( email )

2-1, Rokkodai
Nada-Ku
Kobe, Hyogo, 657-8501
Japan

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