Community Detection in Partial Correlation Network Models

Recognition Program

Authors: Christian Brownlees, Guðmundur Stefán Guðmundsson and Gábor Lugosi

Journal of Business & Economic Statistics, Vol. 40, No 1, 216-226, January, 2022

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.

This paper is acknowledged by the Barcelona School of Economics Recognition Program