发表机构
UC Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对受阶为$n^{-1/2}$的可交换误设定误差污染的GMM，提出经验贝叶斯方法修复局部误设定，通过构造偏差校正估计量与经验贝叶斯估计量，在模拟与实证研究中验证了其优于标准两阶段最小二乘估计的性能。
AI 中文摘要
计量经济学模型为数据生成过程提供了简约但不精确的近似。本文研究当阶为$n^{-1/2}$的可交换误设定误差污染矩条件时的广义矩估计（GMM）。笔者开发了这些误设定误差的均值与方差的估计量，在过度识别约束数量随样本量增长的渐近框架中建立了其一致性。这些超参数估计量被用于构造目标参数的可行偏差校正估计量。笔者还提出了一种经验贝叶斯估计量，该估计量通过从偏差校正估计量中减去一阶估计误差的最佳线性预测，弱改进了精度。利用组合中心极限定理，笔者建立了两种估计量的渐近正态性，并提供了能实现感知误设定的频率学派推断的方差估计量。模拟实验表明，当存在排除约束违反时，该方法可显著优于标准两阶段最小二乘估计。通过重新考察有影响力的Angrist和Krueger（1991）的研究，笔者考虑了一组存在可交换排除约束违反的工具变量。对教育收益率的两阶段最小二乘估计进行修复后，估计值向普通最小二乘方向移动，且降低了对控制变量设定的敏感性。
英文摘要
Econometric models offer parsimonious but inexact approximations to data-generating processes. This paper studies the generalized method of moments (GMM) when exchangeable specification errors of order $n^{-1/2}$ contaminate the moment conditions. I develop estimators for the mean and variance of these specification errors, establishing their consistency in an asymptotic framework where the number of overidentifying restrictions grows with the sample size. These hyperparameter estimates are used to develop a feasible bias-corrected estimator of target parameters. I also propose an empirical Bayes estimator that weakly improves precision by subtracting a best linear predictor of the first-order estimation error from the bias-corrected estimator. Using a combinatorial central limit theorem, I establish asymptotic normality of both estimators and provide variance estimators that enable misspecification-aware frequentist inference. Simulation exercises indicate the procedures can meaningfully improve on standard two-stage least squares estimation when exclusion violations are present. Revisiting the influential study of Angrist and Krueger (1991), I consider an instrument set where exchangeable excludability violations are plausible. Repairing the two-stage least squares estimates of the returns to schooling moves them in the direction of ordinary least squares and reduces sensitivity to the specification of controls.
