交互高维约束下的一致有效推断
Uniformly Valid Inference Under Interactive and High-Dimensional Constraints
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中文总结 AI 辅助
本文针对极值估计量在参数边界处渐近正态性失效问题，提出适用于高维交互约束的拟无约束估计量检验方法，将其应用于面板数据网络估计，实现一致有效推断。
中文摘要 AI 辅助
当极值估计量的参数真实值处于参数空间边界或接近边界时，渐近正态性近似通常不成立。本文分析并开发了使用拟无约束估计量的检验方法，该估计量即使在参数真实向量接近或处于边界时仍具有渐近正态性。这些结果扩展了该估计量的前期工作，允许更多类型的约束，并展示了当干扰参数也为高维时，该方法如何自然地被修改。本文证明，只要初始约束估计量足够准确，Wald检验、似然比检验和拉格朗日乘子检验的变体可在一致意义上控制检验水平。最后，本文将该方法应用于包含面板数据网络估计的场景。
英文摘要
Asymptotic normality approximations often fail to hold for extremum estimators when the true value of the parameter is at or close to the boundary of a parameter space. I analyze and develop tests using a quasi-unconstrained estimator, which is asymptotically normal even when the true parameter vector is near or at the boundary. These results generalize previous work with this estimator by allowing for more types of constraints and showing how the method can naturally be modified when a nuisance parameter is also high-dimensional. I show that variations of Wald, Likelihood Ratio, and Lagrange Multiplier tests can control size in a uniform sense, provided the initial constrained estimator is sufficiently accurate. Lastly, I apply the method to an application involving network estimation with panel data.
