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On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator

1
Melbourne Business School, University of Melbourne, 200 Leicester Street, Carlton, Victoria 3053, Australia
2
Graduate School of Economics, Kobe University, 2-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
*
Author to whom correspondence should be addressed.
Econometrics 2019, 7(1), 15; https://doi.org/10.3390/econometrics7010015
Received: 18 October 2018 / Revised: 18 March 2019 / Accepted: 18 March 2019 / Published: 20 March 2019
This paper investigates the asymptotic properties of a penalized empirical likelihood estimator for moment restriction models when the number of parameters ( p n ) and/or the number of moment restrictions increases with the sample size. Our main result is that the SCAD-penalized empirical likelihood estimator is n / p n -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 n / p n -consistency and an oracle property are satisfied simultaneously. As far as we know, this paper is the first to specify sufficient conditions for both n / p n -consistency and the oracle property of the penalized empirical likelihood estimator. View Full-Text
Keywords: diverging number of parameters; penalized empirical likelihood; sparse models diverging number of parameters; penalized empirical likelihood; sparse models
MDPI and ACS Style

Ando, T.; Sueishi, N. On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator. Econometrics 2019, 7, 15.

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