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Revealing Cluster Structures Based on Mixed Sampling Frequencies


  • Yeonwoo Rho
  • Yun Liu
  • Hie Joo Ahn


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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  • Yeonwoo Rho & Yun Liu & Hie Joo Ahn, 2020. "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Papers 2004.09770,, revised Feb 2021.
  • Handle: RePEc:arx:papers:2004.09770

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    References listed on IDEAS

    1. Pierre Perron & Mototsugu Shintani & Tomoyoshi Yabu, 2017. "Testing for Flexible Nonlinear Trends with an Integrated or Stationary Noise Component," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 822-850, October.
    2. Liangjun Su & Zhentao Shi & Peter C. B. Phillips, 2016. "Identifying Latent Structures in Panel Data," Econometrica, Econometric Society, vol. 84, pages 2215-2264, November.
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    6. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    7. Ralf Becker & Walter Enders & Junsoo Lee, 2006. "A Stationarity Test in the Presence of an Unknown Number of Smooth Breaks," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(3), pages 381-409, May.
    8. Lee, Jim, 2000. "The Robustness of Okun's Law: Evidence from OECD Countries," Journal of Macroeconomics, Elsevier, vol. 22(2), pages 331-356, April.
    9. Paulo M. M. Rodrigues & A. M. Robert Taylor, 2012. "The Flexible Fourier Form and Local Generalised Least Squares De-trended Unit Root Tests-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(5), pages 736-759, October.
    10. Walter Enders & Junsoo Lee, 2012. "A Unit Root Test Using a Fourier Series to Approximate Smooth Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(4), pages 574-599, August.
    11. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307,, revised Feb 2021.
    12. JÖrg Breitung & Christoph Roling, 2015. "Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 588-603, November.
    13. Shujie Ma & Jian Huang, 2017. "A Concave Pairwise Fusion Approach to Subgroup Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 410-423, January.
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