Sparse Bayesian Vector Autoregressions in Huge Dimensions

Kastner, Gregor ORCID: and Huber, Florian ORCID: (2020) Sparse Bayesian Vector Autoregressions in Huge Dimensions. Journal of Forecasting, 39 (7). pp. 1142-1165. ISSN 1099-131X

Available under License Creative Commons: Attribution 4.0 International (CC BY 4.0).

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We develop a Bayesian vector autoregressive (VAR) model with multivariate stochastic volatility that is capable of handling vast dimensional information sets. Three features are introduced to permit reliable estimation of the model. First, we assume that the reduced‐form errors in the VAR feature a factor stochastic volatility structure, allowing for conditional equation‐by‐equation estimation. Second, we apply recently developed global‐local shrinkage priors to the VAR coefficients to cure the curse of dimensionality. Third, we utilize recent innovations to efficiently sample from high‐dimensional multivariate Gaussian distributions. This makes simulation‐based fully Bayesian inference feasible when the dimensionality is large but the time series length is moderate. We demonstrate the merits of our approach in an extensive simulation study and apply the model to US macroeconomic data to evaluate its forecasting capabilities.

Item Type: Article
Keywords: Dirichlet‐Laplace prior, efficient MCMC, factor stochastic volatility, normal‐Gamma prior, shrinkage
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 22 Apr 2020 13:42
Last Modified: 21 Oct 2020 15:04
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