On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator
Melbourne Business School, University of Melbourne, 200 Leicester Street, Carlton, Victoria 3053, Australia
Graduate School of Economics, Kobe University, 2-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
Author to whom correspondence should be addressed.
Received: 18 October 2018 / Revised: 18 March 2019 / Accepted: 18 March 2019 / Published: 20 March 2019
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This paper investigates the asymptotic properties of a penalized empirical likelihood estimator for moment restriction models when the number of parameters (
) and/or the number of moment restrictions increases with the sample size. Our main result is that the SCAD-penalized empirical likelihood estimator is
-consistent under a reasonable condition on the regularization parameter. Our consistency rate is better than the existing ones. This paper also provides sufficient conditions under which
-consistency and an oracle property are satisfied simultaneously. As far as we know, this paper is the first to specify sufficient conditions for both
-consistency and the oracle property of the penalized empirical likelihood estimator.
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MDPI and ACS Style
Ando, T.; Sueishi, N. On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator. Econometrics 2019, 7, 15.
Ando T, Sueishi N. On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator. Econometrics. 2019; 7(1):15.
Ando, Tomohiro; Sueishi, Naoya. 2019. "On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator." Econometrics 7, no. 1: 15.
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