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High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data

Jinyuan Chang, Song Chen and Xiaohong Chen ()

MPRA Paper from University Library of Munich, Germany

Abstract: This paper considers the maximum generalized empirical likelihood (GEL) estimation and inference on parameters identified by high dimensional moment restrictions with weakly dependent data when the dimensions of the moment restrictions and the parameters diverge along with the sample size. The consistency with rates and the asymptotic normality of the GEL estimator are obtained by properly restricting the growth rates of the dimensions of the parameters and the moment restrictions, as well as the degree of data dependence. It is shown that even in the high dimensional time series setting, the GEL ratio can still behave like a chi-square random variable asymptotically. A consistent test for the over-identification is proposed. A penalized GEL method is also provided for estimation under sparsity setting.

Keywords: Generalized empirical likelihood; High dimensionality; Penalized likelihood; Variable selection; Over-identification test; Weak dependence. (search for similar items in EconPapers)
JEL-codes: C1 C13 C14 (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-ecm
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Journal Article: High dimensional generalized empirical likelihood for moment restrictions with dependent data (2015) Downloads
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