Revealing Cluster Structures Based on Mixed Sampling Frequencies
Yeonwoo Rho, Yun Liu, & Hie Joo Ahn
This paper proposes a new nonparametric mixed data sampling (MIDAS) model & develops a framework to infer clusters in a panel regression with mixed frequency data. The nonparametric MIDAS estimation method is more exible & 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 & 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. & uncovers four meaningful clusters based 10 on the dynamic features of state-level labor markets.
Keywords: Clustering; forecasting; mixed data sampling regression model; panel data; penal- ized regression.
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September 23, 2020