A New Class of Tests for Overidentifying Restrictions in Moment Condition Models

In this paper, we propose a new class of tests for overidentifying restrictions in moment condition models. The tests in this new class are quite easy to com- pute. They avoid the complicated saddle point problem in generalized empirical likelihood (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. 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.