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.

翻译：我们建议一个框架,用于估计和推断模型可能错误描述的模型。 我们依靠一种局部的零用药方法,根据样本大小将误差程度指数化。 我们根据一步骤的调整,在参考模型的附近地区构建了平均正方误差为最小值的估测器。 此外, 我们提供含有本地误判下真实参数的互信间隔。 作为解释误判程度的工具, 我们将其映射为参考模型规格测试的当地力量。 我们的方法允许在可能部分或全部地确定相关参数时进行系统敏感度分析。 作为示例, 我们研究三种应用: 对墨西哥有条件现金转移转移的影响进行实证分析, 其误判因方案存在污名效应而导致的误判; 跨部门二进制选择模型, 误判错误分布; 动态面数据二进选模式, 其时间间隔小, 个别影响分布错误描述。