Bayesian nonparametric sparse VAR models

High <THESTERM>dimensional vector</THESTERM> autoregressive (VAR) models require a large number of parameters to be estimated and may suffer of inferential problems. We propose a new <THESTERM>Bayesian</THESTERM> nonparametric (BNP) Lasso <THESTERM>prior</THESTERM> (BNP-Lasso) for high-dimensional VAR models that can improve estimation efficiency and prediction accuracy. Our hierarchical prior overcomes overparametrization and overfitting issues by clustering the VAR coefficients into groups and by shrinking the coefficients of each group toward a common location. Clustering and shrinking effects induced by the BNP-Lasso prior are well suited for the extraction of causal networks from time series, since they account for some stylized facts in real-world networks, which are sparsity, communities structures and heterogeneity in the edges intensity. In order to fully capture the richness of the data and to achieve a better understanding of financial and <THESTERM>macroeconomic</THESTERM> risk, it is therefore crucial that the model used to extract network accounts for these stylized facts. (C) 2019 Elsevier B.V. All rights reserved: