论文标题
时刻不平等的推断：限制的力矩选择过程
Inference for Moment Inequalities: A Constrained Moment Selection Procedure
论文作者
论文摘要
在许多经济学领域，瞬间不等式定义参数的模型中的推论。本文开发了一种新方法，用于改善有限样本中广义力矩选择（GMS）测试程序的性能。该方法通过在其力矩选择步骤中倾斜经验分布来修改GMS测试，以最大程度地提高经验可能性受到零假设的限制的经验可能性。我们表征了一组人口分布，其中修改的GMS测试（i）渐近等同于其非修饰版本与一阶相当，并且（ii）在样本大小足够大时根据本地功率优于其非修饰版本。该提出的修改的一个重要特征是，即使时刻不等式的数量很大，它在计算上仍然是可行的。我们报告的仿真结果表明了修改后的测试尺寸很好，并显着提高了其非修饰对应物的局部功率。
Inference in models where the parameter is defined by moment inequalities is of interest in many areas of economics. This paper develops a new method for improving the performance of generalized moment selection (GMS) testing procedures in finite-samples. The method modifies GMS tests by tilting the empirical distribution in its moment selection step by an amount that maximizes the empirical likelihood subject to the restrictions of the null hypothesis. We characterize sets of population distributions on which a modified GMS test is (i) asymptotically equivalent to its non-modified version to first-order, and (ii) superior to its non-modified version according to local power when the sample size is large enough. An important feature of the proposed modification is that it remains computationally feasible even when the number of moment inequalities is large. We report simulation results that show the modified tests control size well, and have markedly improved local power over their non-modified counterparts.
