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Penalized generalized empirical likelihood in high-dimensional weakly dependent data

Jia Zhang, Haoming Shi, Lemeng Tian and Fengjun Xiao

Journal of Multivariate Analysis, 2019, vol. 171, issue C, 270-283

Abstract: In this paper, we propose a penalized generalized empirical likelihood (PGEL) approach based on the smoothed moment functions Anatolyev (2005), Smith (1997), Smith (2004) for parameters estimation and variable selection in the growing (high) dimensional weakly dependent time series setting. The dimensions of the parameters and moment restrictions are both allowed to grow with the sample size at some moderate rates. The asymptotic properties of the estimators of the smoothed generalized empirical likelihood (SGEL) and its penalized version (SPGEL) are then obtained by properly restricting the degree of data dependence. It is shown that the SPGEL estimator maintains the oracle property despite the existence of data dependence and growing (high) dimensionality. We finally present simulation results and a real data analysis to illustrate the finite-sample performance and applicability of our proposed method.

Keywords: High-dimensional data analysis; Penalized likelihood; Smoothed generalized empirical likelihood; Smoothed moment functions; Weak dependence (search for similar items in EconPapers)
Date: 2019
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