Sparse Bayesian Vector Autoregressions in Huge Dimensions

Gregor Kastner, Forian Huber

Publication: Scientific journalJournal articlepeer-review

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.
Original languageEnglish
Pages (from-to)1142 - 1165
JournalJournal of Forecasting
Volume39
DOIs
Publication statusPublished - 2020

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 101018 Statistics
  • 502025 Econometrics
  • 101026 Time series analysis

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