Robust Inference for Nonlinear Moment Condition Models with Possible Weak Identification

该奖项将资助开发分析经济数据的新方法的工作。 研究经济数据的研究人员在进行统计推断时经常依赖于渐近近似。然而，我们知道，标准的渐近理论失败时，识别是弱的。 因此，这些近似值在许多情况下不是非常准确。PI提出了一个可能的解决方案。这些新方法有可能用于各种应用。该项目通过开发对某些经济理论进行正式统计检验的新方法来推动科学发展。PI计划开发非线性广义矩量法（GMM）模型的检验程序，这些模型在几乎不受限制的参数空间中具有正确的渐近大小，但矩函数及其导数的矩界一致。 该检验是条件似然比（Conditional Likelihood Ratio，简写为CLR）检验的一种推广形式。然后，他想证明这种测试在某些模型和测试类别的功效方面具有（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.