We consider inference in models defined by approximate moment conditions. We show that near‐optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias and, therefore, differs from the one that is optimal under correct specification. To formally show the near‐optimality of these CIs, we develop asymptotic efficiency bounds for inference in the locally misspecified GMM setting. These bounds may be of independent interest, due to their implications for the possibility of using moment selection procedures when conducting inference in moment condition models. We apply our methods in an empirical application to automobile demand, and show that adjusting the weighting matrix can shrink the CIs by a factor of 3 or more.

中文翻译：

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使用近似矩条件模型的灵敏度分析

我们考虑由近似力矩条件定义的模型中的推论。我们表明，可以通过采用广义矩量（GMM）估计器，并在标准误差乘以临界值的基础上加上和减去一个临界值，来形成近似最佳的置信区间（CIs），该临界值考虑了矩条件错误指定的潜在偏差。为了在潜在错误指定条件下优化性能，此GMM估算器的权重矩阵考虑到了这一潜在偏差，因此与正确规范下的最佳偏差有所不同。为了正式显示这些配置项的接近最优性，我们开发了渐近效率边界以在本地错误指定的GMM设置中进行推断。这些界限可能具有独立利益，由于它们暗示了在矩条件模型中进行推理时可能使用矩选择程序的可能性。我们将我们的方法应用于汽车需求的经验应用，并表明调整权重矩阵可以将CI缩小3倍或更多。