A new class of tests for overidentifying restrictions in moment condition models
提出了一类新的过度识别检验方法，将Neyman的C(α)检验扩展到广义经验似然框架，仅需n一致估计量，避免了复杂的鞍点问题，蒙特卡洛模拟显示其有限样本表现优于两步GMM检验。
In this study, we propose a new class of tests for overidentifying restrictions in moment condition models, extending Neyman’s (1959 Neyman, J. (1959). Optimal asymptotic tests of composite statistical hypotheses. In: Grenander, U., ed., Probability and Statistics, New York: Wiley. [Google Scholar]) C(α) test for parameter hypotheses in maximum likelihood to generalized empirical likelihood (GEL). These tests lack the complicated saddle point problem seen in GEL estimation; only a n consistent estimator, where n is the sample size, is needed. In addition to discussing their first-order properties, we establish that under some regularity conditions, these tests share the same higher-order properties as GEL overidentifying tests, given proper consistent estimators. A Monte Carlo simulation study shows that the new class of tests of overidentifying restrictions has better finite sample performance than the two-step GMM overidentification test, and compares well to several potential alternatives in terms of overall performance.