Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood
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 estim...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/391719 |
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| _version_ | 1841524660864286720 |
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| author | Yunquan Song |
| author_facet | Yunquan Song |
| author_sort | Yunquan Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c105b51c872d491d9f41fd60a790141e |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-c105b51c872d491d9f41fd60a790141e2025-02-03T05:47:49ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/391719391719Moment Conditions Selection Based on Adaptive Penalized Empirical LikelihoodYunquan Song0China University of Petroleum, Qingdao 266580, ChinaEmpirical 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.http://dx.doi.org/10.1155/2014/391719 |
| spellingShingle | Yunquan Song Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood Abstract and Applied Analysis |
| title | Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood |
| title_full | Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood |
| title_fullStr | Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood |
| title_full_unstemmed | Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood |
| title_short | Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood |
| title_sort | moment conditions selection based on adaptive penalized empirical likelihood |
| url | http://dx.doi.org/10.1155/2014/391719 |
| work_keys_str_mv | AT yunquansong momentconditionsselectionbasedonadaptivepenalizedempiricallikelihood |