Robust Inference for Nonlinear Moment Condition Models with Possible Weak Identification

该奖项基金的工作将开发分析经济数据的新方法。研究经济数据的研究人员在进行统计推断时往往依赖于渐近。然而，我们知道，当辨识力较弱时，标准的渐近理论就会失效。因此，这些近似值在许多情况下并不十分准确。PI提出了一种可能的解决方案。这些新方法可能会在各种应用中使用。该项目通过开发对某些经济理论进行形式统计检验的新方法来促进科学的发展。PI计划开发非线性广义矩方法(GMM)模型的测试程序，这些模型对几乎不受限制的参数空间具有正确的渐近大小，但矩函数及其导数的一致矩界限除外。该检验是条件似然比(CLR)检验的推广形式。然后，他想要证明，就某些模型和测试类别的功率而言，这种测试具有(接近)最优性。

The award funds work that will develop new methods for analyzing economic data. Researchers working with economic data often rely on asymptotic approximations in making statistical inferences. However, we know that standard asymptotic theory fails when identification is weak. Therefore these approximations are not very accurate in a number of cases. The PI proposes a possible solution. The new methods could potentially be used in a variety of applications. The project advances science by developing new methods for conducting formal statistical tests of certain kinds of economic theory.The PI plans to develop testing procedures in nonlinear Generalized Method of Moments (GMM) models that have correct asymptotic size for a virtually unrestricted parameter space, except for uniform moment bounds on the moment function and its derivative. The proposed test is a generalized form of the Conditional Likelihood Ratio (CLR) test. He then wants to show that this tests have (near) optimality properties in terms of power for certain models and classes of tests.