基于混合采样频率的聚类结构揭示
Revealing Cluster Structures Based on Mixed Sampling Frequencies
摘要：
本文提出了一个新的线性混合数据抽样模型（MIDAS），并开发了一个框架，以推断集群的面板回归与混合频率数据。线性化MIDAS估计方法比竞争方法更灵活，实现起来也简单得多。我们表明，所提出的聚类算法成功地恢复真正的成员在横截面，无论是在理论上和模拟，而不需要事先知道的集群的数量。这种方法适用于混合频率奥肯定律模型在美国的国家级数据，并揭示了四个有意义的集群的基础上，国家级的劳动力市场的动态特征。
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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