干扰投影后的识别与信息
Identification and Information after Nuisance Projection
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中文总结 AI 辅助
该研究针对双向依赖下经方程兼容干扰投影的线性面板IV，推导了弱识别极限等推断方法，模拟及国际货币应用显示干扰投影可揭示有效信息，需在干扰移除后评估识别。
中文摘要 AI 辅助
实证研究中，在估计结构关系前常移除固定效应、潜在因子或高维控制变量，这些变换虽减少混淆但也可能消除识别变异。我们研究双向依赖下经方程兼容干扰投影后的线性面板工具变量（IV）：投影雅可比矩阵决定哪些结构方向仍可见，投影得分法则决定其精度；在高斯固定秩层上，这些方向结合形成投影信息矩阵。我们推导了具有维度特异性信息积累的弱识别极限、可行因子转移条件、识别稳健检验、非高斯交互极限的自助法程序，以及针对投影谱、秩、子空间和信息矩阵的推断。模拟结果显示，原始第一阶段统计量高于500时可能支持错误符号，而投影诊断揭示弱有效信息；在国际货币应用中，常见投影大幅减弱了表面的外国产出持续性，而高斯参考安德森-鲁宾（Anderson--Rubin）集仍无界。因此，应在移除干扰后评估识别情况。
英文摘要
Empirical work often removes fixed effects, latent factors, or high-dimensional controls before estimating structural relationships. These transformations reduce confounding but may also remove identifying variation. We study linear panel IV after one equation-compatible nuisance projection under two-way dependence. The projected Jacobian determines which structural directions remain visible; the projected-score law determines their precision; and, on Gaussian fixed-rank strata, they combine in a Projected Information Matrix. We derive weak-identification limits with dimension-specific information accumulation, feasible factor-transfer conditions, identification-robust tests, bootstrap procedures for non-Gaussian interaction limits, and inference for the projected spectrum, rank, subspaces, and information matrix. Simulations show that a raw first-stage statistic above 500 can support the wrong sign while projected diagnostics reveal weak valid information. In an international monetary application, common projection substantially attenuates apparent foreign-output persistence, while Gaussian-reference Anderson--Rubin sets remain unbounded. Identification should therefore be assessed after nuisance removal.
