 Detecting groups in large vector autoregressionsGuðmundur Guðmundsson and Christian Brownlees Journal of Econometrics, 2021, vol. 225, issue 1, 2-26 Abstract: 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; Forecasting (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:225:y:2021:i:1:p:2-26 DOI: 10.1016/j.jeconom.2021.03.012 Access Statistics for this article Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson More articles in Journal of Econometrics from Elsevier |
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