SECOND-ORDER REFINEMENT OF EMPIRICAL LIKELIHOOD FOR TESTING OVERIDENTIFYING RESTRICTIONS
研究了经验似然过度识别检验的二阶性质，发现其具有Bartlett可修正性，并提出了基于经验Bartlett校正和调整经验似然的二阶精炼方法，模拟表明这些方法能改善零假设拒绝概率的性质。
This paper studies second-order properties of the empirical likelihood overidentifying restriction test to check the validity of moment condition models. We show that the empirical likelihood test is Bartlett correctable and suggest second-order refinement methods for the test based on the empirical Bartlett correction and adjusted empirical likelihood. Our second-order analysis supplements the one in Chen and Cui (2007, Journal of Econometrics 141, 492–516) who considered parameter hypothesis testing for overidentified models. In simulation studies we find that the empirical Bartlett correction and adjusted empirical likelihood assisted by bootstrapping provide reasonable improvements for the properties of the null rejection probabilities.