Revealing Cluster Structures Based on Mixed Sampling Frequencies

Hie Joo Ahn, Yun Liu (yliu26@mtu.edu) and Yeonwoo Rho (yrho@mtu.edu)
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Hie Joo Ahn: https://www.federalreserve.gov/econres/hie-joo-ahn.htm
Yeonwoo Rho: https://www.mtu.edu/math/department/faculty-staff/faculty/rho/

No 2020-082, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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 (search for similar items in EconPapers)
JEL-codes: C14 C32 C33 C53 J64 (search for similar items in EconPapers)
Pages: 69
Date: 2020-09-23
New Economics Papers: this item is included in nep-lab and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2020-82

DOI: 10.17016/FEDS.2020.082

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