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.
翻译:本文件提出了一个新的线性混合数据抽样模式,并制定了一个框架,用以在混合频率数据的小组回归中推断组群。线性MIDAS估算方法比竞争方法更灵活,实施得更简单得多。我们表明,拟议的群集算法在理论和模拟中成功地恢复了跨部门的真正成员,而无需事先了解群集的数量。这种方法适用于美国州一级数据的混合频欧昆法律模型,并发现了基于州一级劳动力市场动态特征的四个有意义的组群。