Measuring the Sensitivity of Parameter Estimates to Estimation Moments*
提出一种低成本计算参数估计对数据矩敏感性的方法，帮助读者预测识别假设违反时结果的变化，并推广了非线性模型中的遗漏变量偏误公式。
Abstract We propose a local measure of the relationship between parameter estimates and the moments of the data they depend on. Our measure can be computed at negligible cost even for complex structural models. We argue that reporting this measure can increase the transparency of structural estimates, making it easier for readers to predict the way violations of identifying assumptions would affect the results. When the key assumptions are orthogonality between error terms and excluded instruments, we show that our measure provides a natural extension of the omitted variables bias formula for nonlinear models. We illustrate with applications to published articles in several fields of economics.