Sparse Bayesian vector autoregressions in huge dimensions

Gregor Kastner and Florian Huber

Papers from arXiv.org

Abstract: 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.

Date: 2017-04, Revised 2019-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (18)

Published in Journal of Forecasting (2020)

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Journal Article: Sparse Bayesian vector autoregressions in huge dimensions (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1704.03239

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