摘要翻译：
我们提出了一个当模型可能被错误描述时的估计和推断框架。我们依赖于一种局部渐近方法，其中错误规格的程度由样本量索引。基于一步调整，我们构造了在参考模型邻域内均方误差为极小极大的估计量。此外，我们还提供了在局部错误规范下包含真参数的置信区间。作为解释错误规范程度的工具，我们将其映射到参考模型的规范测试的局部功率。当感兴趣的参数可能被部分或不规则地识别时，我们的方法允许系统的敏感性分析。作为例证，我们研究了三个应用：墨西哥有条件现金转移影响的实证分析，其中错误描述源于方案的污名效应的存在；横截面二元选择模型，其中错误分布被错误描述；动态面板数据二元选择模型，其中时间周期数较少，个体效应分布被错误描述。
---
英文标题：
《Minimizing Sensitivity to Model Misspecification》
---
作者：
St\'ephane Bonhomme, Martin Weidner
---
最新提交年份：
2021
---
分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
--

---
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.
---
PDF链接：
https://arxiv.org/pdf/1807.02161