摘要翻译：
本文研究了弱辨识GMM模型的最优决策规则，包括估计量和检验。我们推导了弱辨识GMM的极限实验，并提出了一个理论上有动机的先验类，作为极限情形，它产生了准Bayes决策规则。与以前文献中的结果一起，这为准贝叶斯方法建立了理想的性质，无论模型识别状态如何，我们推荐准贝叶斯用于识别是一个问题的设置。我们进一步提出了加权平均功率最优辨识鲁棒频率检验和置信度集，并证明了弱辨识下准Bayes后验的Bernstein-von Mises型结果。
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英文标题：
《Optimal Decision Rules for Weak GMM》
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作者：
Isaiah Andrews and Anna Mikusheva
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最新提交年份：
2021
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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：
  This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically-motivated class of priors which give rise to quasi-Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi-Bayes approach regardless of model identification status, and we recommend quasi-Bayes for settings where identification is a concern. We further propose weighted average power-optimal identification-robust frequentist tests and confidence sets, and prove a Bernstein-von Mises-type result for the quasi-Bayes posterior under weak identification.
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PDF链接：
https://arxiv.org/pdf/2007.04050