摘要翻译：
对于经验似然、指数倾斜和指数倾斜经验似然估计，我提出了一个非参数iid引导过程，该过程实现了t检验和置信区间的渐近精化，以及基于这些估计的Wald检验和置信区域的渐近精化。此外，所提出的bootstrap对模型错误规范具有鲁棒性，即无论假设的矩条件模型是否正确指定，它都能实现渐近精化。这个结果是新的，因为即使在正确的模型规范下，基于这些估计量的bootstrap的渐近精化也没有在文献中建立。在动态面板数据中进行了蒙特卡罗实验，以支持理论结果。作为应用，计算了Hellerstein和Imbens(1999)的就学收益率的bootstrap置信区间。结果表明，学校教育的回报可能更高。
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英文标题：
《Asymptotic Refinements of a Misspecification-Robust Bootstrap for
  Generalized Empirical Likelihood 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 procedure for the empirical likelihood, the exponential tilting, and the exponentially tilted empirical likelihood estimators that achieves asymptotic refinements for t tests and confidence intervals, and Wald tests and confidence regions based on such estimators. Furthermore, the proposed bootstrap is robust to model misspecification, i.e., it achieves asymptotic refinements regardless of whether the assumed moment condition model is correctly specified or not. This result is new, because asymptotic refinements of the bootstrap based on these estimators have not been established in the literature even under correct model specification. Monte Carlo experiments are conducted in dynamic panel data setting to support the theoretical finding. As an application, bootstrap confidence intervals for the returns to schooling of Hellerstein and Imbens (1999) are calculated. The result suggests that the returns to schooling may be higher.
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PDF链接：
https://arxiv.org/pdf/1806.00953