SECOND-ORDER REFINEMENT OF EMPIRICAL LIKELIHOOD FOR TESTING OVERIDENTIFYING RESTRICTIONS
SECOND-ORDER REFINEMENT OF EMPIRICAL LIKELIHOOD FOR TESTING OVERIDENTIFYING RESTRICTIONS
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DOI：
10.1017/s0266466612000369
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发表时间：
2012-01
影响因子：
0.8
通讯作者：
Yukitoshi Matsushita;Taisuke Otsu
中科院分区：
文献类型：
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作者：
Yukitoshi Matsushita;Taisuke Otsu
本文研究了经验似然过度识别限制检验的二阶性质，以检验矩条件模型的有效性。我们证明经验似然检验是 Bartlett 可校正的，并建议基于经验 Bartlett 校正和调整的经验似然的检验的二阶细化方法。我们的二阶分析补充了 Chen 和 Cui (2007, Journal of Econometrics141, 492-516) 的分析，他们考虑了过度识别模型的参数假设检验。在模拟研究中，我们发现经验 Bartlett 校正和引导辅助的调整经验似然为零拒绝概率的属性提供了合理的改进。
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 Econometrics141, 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.