Quotation Kastner, Gregor, Huber, Forian. 2020. Sparse Bayesian Vector Autoregressions in Huge Dimensions. Journal of Forecasting. 39 1142-1165.




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.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Forecasting
Citation Index SSCI
WU Journalrating 2009 A
WU-Journal-Rating new FIN-A, INF-A, MAR-B, STRAT-B, VW-D, WH-B
Language English
Title Sparse Bayesian Vector Autoregressions in Huge Dimensions
Volume 39
Year 2020
Page from 1142
Page to 1165
Reviewed? Y
URL https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2680
DOI https://doi.org/10.1002/for.2680
Open Access Y
Open Access Link https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2680


High-dimensional statistical learning: New methods to advance economic and sustainability policies
Kastner, Gregor (Details)
Huber, Forian (University of Salzburg, Austria)
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1162 Statistics (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
5707 Time series analysis (Details)
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