Revealing Cluster Structures Based on Mixed Sampling Frequencies

Ahn, Hie Joo; Liu, Yun; Rho, Yeonwoo

doi:10.17016/FEDS.2020.082

Working Paper

Revealing Cluster Structures Based on Mixed Sampling Frequencies

Abstract: This paper proposes a new nonparametric mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The nonparametric 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 10 on the dynamic features of state-level labor markets.

Keywords: Clustering; Forecasting; Mixed data sampling regression model; Panel data; Penalized regression;

JEL Classification: C14; C32; C33; C53; J64;

https://doi.org/10.17016/FEDS.2020.082