摘要翻译：
本文提出了一种新的线性化混合数据抽样(MIDAS)模型，并建立了一个在混合频率数据的面板回归中进行聚类推断的框架。线性化的MIDAS估计方法比其他方法更灵活，实现起来也更简单。理论和仿真结果表明，该聚类算法在不需要预先知道聚类数目的情况下，成功地恢复了横截面上的真实隶属度。该方法被应用于美国州级数据的混频奥肯定律模型，并基于州级劳动力市场的动态特征揭示了四个有意义的聚类。
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英文标题：
《Revealing Cluster Structures Based on Mixed Sampling Frequencies》
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作者：
Yeonwoo Rho, Yun Liu, and Hie Joo Ahn
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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：
  This paper proposes a new linearized mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The linearized MIDAS estimation method is more flexible and substantially simpler to implement than competing approaches. We show that the proposed clustering algorithm successfully recovers true membership in the cross-section, both in theory and in simulations, without requiring prior knowledge of the number of clusters. This methodology is applied to a mixed-frequency Okun's law model for state-level data in the U.S. and uncovers four meaningful clusters based on the dynamic features of state-level labor markets.
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PDF链接：
https://arxiv.org/pdf/2004.09770