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

Author

Listed:
  • Tomohiro Ando

    (Melbourne Business School, University of Melbourne, 200 Leicester Street, Carlton, Victoria 3053, Australia)

  • Naoya Sueishi

    (Graduate School of Economics, Kobe University, 2-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan)

Abstract

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.

Suggested Citation

  • Tomohiro Ando & Naoya Sueishi, 2019. "On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator," Econometrics, MDPI, vol. 7(1), pages 1-14, March.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:1:p:15-:d:215602
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    References listed on IDEAS

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