Community Detection in Partial Correlation Network Models

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  • Christian Brownlees, Barcelona Graduate School of Economics
  • ,
  • Gudmundur Stefan Gudmundsson
  • Gabor Lugosi, Universitat Pompeu Fabra, Barcelona Graduate School of Economics

We introduce a class of partial correlation network models with a community structure for large panels of time series. In the model, the series are partitioned into latent groups such that correlation is higher within groups than between them. We then propose an algorithm that allows one to detect the communities using the eigenvectors of the sample covariance matrix. We study the properties of the procedure and establish its consistency. The methodology is used to study real activity clustering in the United States.

OriginalsprogEngelsk
TidsskriftJournal of Business and Economic Statistics
Antal sider11
ISSN0735-0015
DOI
StatusE-pub ahead of print - 2020

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