Abstract and Applied Analysis

Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood

Yunquan Song

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Abstract

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.

Article information

Source
Abstr. Appl. Anal., Volume 2014, Special Issue (2014), Article ID 391719, 16 pages.

Dates
First available in Project Euclid: 6 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412606359

Digital Object Identifier
doi:10.1155/2014/391719

Mathematical Reviews number (MathSciNet)
MR3240536

Zentralblatt MATH identifier
07022292

Citation

Song, Yunquan. Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 391719, 16 pages. doi:10.1155/2014/391719. https://projecteuclid.org/euclid.aaa/1412606359


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