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Detecting groups in large vector autoregressions
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DOI：10.1016/j.jeconom.2021.03.012.png)
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Abstract
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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. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Vector autoregressions
Time series
Random graphs
Community detection
Spectral clustering
Forecasting
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