Bayesian nonparametric sparse VAR models

Monica Billio, Roberto Casarin, Luca Rossini

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    Abstract

    High dimensional vector autoregressive (VAR) models require a large number of parameters to be estimated and may suffer of inferential problems. We propose a new Bayesian nonparametric (BNP) Lasso prior (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 macroeconomic risk, it is therefore crucial that the model used to extract network accounts for these stylized facts.
    Original languageEnglish
    Pages (from-to)97-115
    Number of pages19
    JournalJournal of Econometrics
    Volume212
    Issue number1
    Early online date20 Apr 2019
    DOIs
    Publication statusPublished - Sep 2019

    Keywords

    • Bayesian model selection
    • Bayesian nonparametrics
    • Connectedness
    • Large vector autoregression
    • Multilayer networks
    • Network communities
    • Shrinkage

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