Open Access
2014 Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood
Yunquan Song
Abstr. Appl. Anal. 2014(SI11): 1-16 (2014). DOI: 10.1155/2014/391719


Empirical likelihood is a very popular method and has been widely used in the fields of artificial intelligence (AI) and data mining as tablets and mobile application and social media dominate the technology landscape. This paper proposes an empirical likelihood shrinkage method to efficiently estimate unknown parameters and select correct moment conditions simultaneously, when the model is defined by moment restrictions in which some are possibly misspecified. We show that our method enjoys oracle-like properties; that is, it consistently selects the correct moment conditions and at the same time its estimator is as efficient as the empirical likelihood estimator obtained by all correct moment conditions. Moreover, unlike the GMM, our proposed method allows us to carry out confidence regions for the parameters included in the model without estimating the covariances of the estimators. For empirical implementation, we provide some data-driven procedures for selecting the tuning parameter of the penalty function. The simulation results show that the method works remarkably well in terms of correct moment selection and the finite sample properties of the estimators. Also, a real-life example is carried out to illustrate the new methodology.


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Yunquan Song. "Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood." Abstr. Appl. Anal. 2014 (SI11) 1 - 16, 2014.


Published: 2014
First available in Project Euclid: 6 October 2014

zbMATH: 07022292
MathSciNet: MR3240536
Digital Object Identifier: 10.1155/2014/391719

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI11 • 2014
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