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摘要翻译：
我们的置信度集量化了聚类面板模型中数据驱动聚类分配的统计不确定性。它以预先指定的概率联合覆盖所有单元的真实簇成员，并通过反演许多同时进行的单元特定的单边组成员测试来构造。我们利用高维统计中的一些工具在$n，T\\infty$渐近下证明了我们的方法，其中一些工具在本文中得到了扩展或发展。我们给出了一个经验应用以及蒙特卡罗证据，证明置信集在有限样本中具有足够的复盖。
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英文标题：
《Confidence set for group membership》
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作者：
Andreas Dzemski and Ryo Okui
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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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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
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英文摘要：
Our confidence set quantifies the statistical uncertainty from data-driven cluster assignment in clustered panel models. It covers the true cluster memberships jointly for all units with pre-specified probability and is constructed by inverting many simultaneous unit-specific one-sided tests for group membership. We justify our approach under $N, T \to \infty$ asymptotics using tools from high-dimensional statistics, some of which we extend or develop in this paper. We provide an empirical application as well as Monte Carlo evidence that the confidence set has adequate coverage in finite samples.
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PDF链接：
https://arxiv.org/pdf/1801.00332
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