Asymptotically Optimal Tests Using Limited Information and Testing for Exogeneity
在线性动态联立方程模型中，通过将弱外生性联合假设与过度识别约束适当分解，证明子假设可分离，从而利用有限信息统计量构造弱外生性假设的渐近最优检验。
By appropriately partitioning the joint hypothesis of weak exogeneity and the maintained overidentifying restrictions in the linear dynamic simultaneous equations model and showing that the component subhypotheses are separable, asymptotically optimal tests for the weak exogeneity hypothesis may be constructed using limited information statistics. A necessary and sufficient condition for the separability of parametric hypotheses of the mixed implicit function and constraint equation type is derived which generalizes conditions previously obtained in the literature. Consequently, limited and full information procedures for testing the weak exogeneity hypothesis are asymptotically equivalent. The impact of these results for testing strong exogeneity in the linear dynamic simultaneous equations model is also explored.