Detecting Groups in Large Vector Autoregressions

45 Pages Posted: 21 Nov 2017 Last revised: 16 Dec 2021

See all articles by Gudmundur Gudmundsson

Gudmundur Gudmundsson

Aarhus BSS; Aarhus University - CREATES

Christian T. Brownlees

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences

Date Written: November 21, 2017


This work introduces the stochastic block vector autoregressive (SB-VAR) model. In this class of vector autoregressions, the time series are partitioned into latent groups such that spillover effects are stronger among series that belong to the same group than otherwise. A key question that arises in this framework is how to detect the latent groups from a sample of observations generated by the model. To this end, we propose a group detection algorithm based on the eigenvectors of a function of the estimated autoregressive matrices. We establish that the proposed algorithm consistently detects the groups when the cross-sectional and time-series dimensions are sufficiently large. The methodology is applied to study the group structure of a panel of risk measures of top financial institutions in the United States and a panel of word counts extracted from Twitter.

Keywords: Vector Autoregressions, Time Series, Random Graphs, Community Detection, Spectral Clustering, Directed Graphs, Weighted Graphs, Forecasting

JEL Classification: C3, C32, C55

Suggested Citation

Gudmundsson, Gudmundur and Brownlees, Christian T., Detecting Groups in Large Vector Autoregressions (November 21, 2017). Available at SSRN: or

Gudmundur Gudmundsson (Contact Author)

Aarhus BSS ( email )

Fuglesangs Allé 4
Aarhus V, 8210

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C

Christian T. Brownlees

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005


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