最小化对模型误指定的敏感度
Minimizing Sensitivity to Model Misspecification
摘要：
我们提出了一个框架，估计和推理时，模型可能被误指定。我们依赖于一个局部渐近的方法，其中的程度是由样本量的指标。我们构造的估计，其均方误差是极小极大的参考模型的邻域中，一步调整的基础上。此外，我们提供了包含真参数的置信区间。作为一种工具来解释的程度的错误规格，我们将其映射到当地的权力规格测试的参考模型。我们的方法允许系统的敏感性分析时，感兴趣的参数可能是部分或不规则的识别。作为例证，我们研究了三个应用程序：在墨西哥的有条件现金转移的影响的实证分析，其中误指定源于存在的耻辱效应的程序，一个横截面的二元选择模型的错误分布是误指定的，和一个动态的面板数据二元选择模型的时间段的数量是小的，个人的影响分布是误指定的。
英文摘要：
We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one-step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systematic sensitivity analysis when the parameter of interest may be partially or irregularly identified. As illustrations, we study three applications: an empirical analysis of the impact of conditional cash transfers in Mexico where misspecification stems from the presence of stigma effects of the program, a cross-sectional binary choice model where the error distribution is misspecified, and a dynamic panel data binary choice model where the number of time periods is small and the distribution of individual effects is misspecified.
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