摘要翻译：
我提出了一个非参数的iid引导程序，即使在模型被错误指定的情况下，它也能实现基于GMM估计量的t检验和置信区间的渐近精化。此外，我的引导程序不需要重新输入矩函数，这被认为是GMM的关键。不管模型的错误说明，所提出的bootstrap实现了与传统bootstrap方法相同的急剧的精化量，传统bootstrap方法通过在没有错误说明的情况下重新进入来建立渐近精化。其关键思想是将Hall和Inoue(2003)的错误规范下的错误引导矩条件与GMM的大样本理论联系起来。提供了两个例子：组合数据集和无效工具变量。
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英文标题：
《Asymptotic Refinements of a Misspecification-Robust Bootstrap for
  Generalized Method of Moments Estimators》
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作者：
Seojeong Lee
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最新提交年份：
2018
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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。
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英文摘要：
  I propose a nonparametric iid bootstrap that achieves asymptotic refinements for t tests and confidence intervals based on GMM estimators even when the model is misspecified. In addition, my bootstrap does not require recentering the moment function, which has been considered as critical for GMM. Regardless of model misspecification, the proposed bootstrap achieves the same sharp magnitude of refinements as the conventional bootstrap methods which establish asymptotic refinements by recentering in the absence of misspecification. The key idea is to link the misspecified bootstrap moment condition to the large sample theory of GMM under misspecification of Hall and Inoue (2003). Two examples are provided: Combining data sets and invalid instrumental variables.
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PDF链接：
https://arxiv.org/pdf/1806.01450