Bayesian Nonparametric Sparse Vector Autoregressive Models

Monica Billio (), Roberto Casarin () and Luca Rossini ()
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Monica Billio: University Ca’ Foscari of Venice
Roberto Casarin: University Ca’ Foscari of Venice
Luca Rossini: Free University of Bozen

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 155-160 from Springer

Abstract: Abstract Seemingly unrelated regression (SUR) models are useful in studying the interactions among economic variables. In a high dimensional setting, these models require a large number of parameters to be estimated and suffer of inferential problems. To avoid overparametrization issues, we propose a hierarchical Dirichlet process prior (DPP) for SUR models, which allows shrinkage of coefficients toward multiple locations. We propose a two-stage hierarchical prior distribution, where the first stage of the hierarchy consists in a lasso conditionally independent prior of the Normal-Gamma family for the coefficients. The second stage is given by a random mixture distribution, which allows for parameter parsimony through two components: the first is a random Dirac point-mass distribution, which induces sparsity in the coefficients; the second is a DPP, which allows for clustering of the coefficients.

Keywords: Bayesian nonparametrics; Bayesian model selection; Shrinkage; Large vector autoregression (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_29

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DOI: 10.1007/978-3-319-89824-7_29

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