This paper proposes a new nonparametric mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel dataset of mixed sampling frequencies. The nonparametric MIDAS estimation method is more flexible but substantially less costly to estimate than existing approaches. The proposed clustering algorithm successfully recovers true membership in the cross-section  both in theory and in simulations  without requiring prior knowledge such as the number of clusters. This methodology is applied to estimate a mixed-frequency Okun's law model for the state-level data in the U.S. and uncovers four  clusters based on the dynamic features of  labor markets.
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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