论文标题
弱GMM的最佳决策规则
Optimal Decision Rules for Weak GMM
论文作者
论文摘要
本文研究了针对弱标识的GMM模型的最佳决策规则，包括估计器和测试。我们得出了弱标识的GMM的极限实验，并提出了一种理论动机的先验类别，从而导致准巴耶斯决策规则作为限制案例。与以前的文献中的结果一起，无论模型识别状态如何，这都建立了准巴约斯方法的理想属性，我们建议用于识别问题的设置。我们进一步提出了加权的平均功率 - 最佳识别式频繁测试和置信度，并证明了在弱识别识别下的准湾后期的伯恩斯坦 - 伏次误差结果。
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.
