Robust Inference for Nonlinear Moment Condition Models with Possible Weak Identification

具有可能弱识别的非线性力矩条件模型的鲁棒推理

• 批准号: 1462707
• 负责人: Patrik Guggenberger
• 金额: $ 25.85万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-15 至 2018-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462707&HistoricalAwards=false
• 关键词: Robust; Inference; Nonlinear; Moment; Condition

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.

该奖项资助将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，当辨识弱时，标准渐近理论失效。因此，这些近似在许多情况下不是很准确。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，推动了科学的发展。PI计划开发非线性广义矩法（GMM）模型的测试程序，该模型在几乎不受限制的参数空间中具有正确的渐近大小，除了矩函数及其导数的均匀矩界。所提出的检验是条件似然比（CLR）检验的一种广义形式。然后，他想表明，就某些模型和测试类别的能力而言，该测试具有（接近）最优性属性。