This paper considers variable selection for moment restriction models. We propose a penalized empirical likelihood （PEL） approach that has desirable asymptotic properties comparable to the penalized likelihood appro...This paper considers variable selection for moment restriction models. We propose a penalized empirical likelihood （PEL） approach that has desirable asymptotic properties comparable to the penalized likelihood approach, which relies on a correct parametric likelihood specification. In addition to being consistent and having the oracle property, PEL admits inference on parameter without having to estimate its estimator＇s covariance. An approximate algorithm, along with a consistent BIC-type criterion for selecting the tuning parameters, is provided for FEL. The proposed algorithm enjoys considerable computational efficiency and overcomes the drawback of the local quadratic approximation of nonconcave penalties. Simulation studies to evaluate and compare the performances of our method with those of the existing ones show that PEL is competitive and robust. The proposed method is illustrated with two real examples.展开更多
In this article we study the empirical likelihood inference for MA(q) model.We propose the moment restrictions,by which we get the empirical likelihood estimator of the model parameter,and we also propose an empirical...In this article we study the empirical likelihood inference for MA(q) model.We propose the moment restrictions,by which we get the empirical likelihood estimator of the model parameter,and we also propose an empirical log-likelihood ratio based on this estimator.Our result shows that the EL estimator is asymptotically normal,and the empirical log-likelihood ratio is proved to be asymptotical standard chi-square distribution.展开更多
基金supported partly by National Natural Science Foundation of China (Grant No. 11071045)Shanghai Leading Academic Discipline Project (Grant No. B210)
文摘This paper considers variable selection for moment restriction models. We propose a penalized empirical likelihood （PEL） approach that has desirable asymptotic properties comparable to the penalized likelihood approach, which relies on a correct parametric likelihood specification. In addition to being consistent and having the oracle property, PEL admits inference on parameter without having to estimate its estimator＇s covariance. An approximate algorithm, along with a consistent BIC-type criterion for selecting the tuning parameters, is provided for FEL. The proposed algorithm enjoys considerable computational efficiency and overcomes the drawback of the local quadratic approximation of nonconcave penalties. Simulation studies to evaluate and compare the performances of our method with those of the existing ones show that PEL is competitive and robust. The proposed method is illustrated with two real examples.
文摘In this article we study the empirical likelihood inference for MA(q) model.We propose the moment restrictions,by which we get the empirical likelihood estimator of the model parameter,and we also propose an empirical log-likelihood ratio based on this estimator.Our result shows that the EL estimator is asymptotically normal,and the empirical log-likelihood ratio is proved to be asymptotical standard chi-square distribution.
