论文标题
根据混合采样频率揭示簇结构
Revealing Cluster Structures Based on Mixed Sampling Frequencies
论文作者
论文摘要
本文提出了一种新的线性化混合数据采样（MIDAS）模型，并开发了一个框架，可以在带有混合频率数据的面板回归中推断簇。与竞争方法相比，线性化的MIDAS估计方法更灵活，实现更简单。我们表明，所提出的聚类算法成功地在理论和模拟中成功地恢复了横截面中的真实成员资格，而无需对簇数的先验知识。该方法应用于美国国家级数据的混合频率Okun法律模型，并根据州级劳动力市场的动态特征揭示了四个有意义的集群。
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.
