bvars: Bayesian Forecasting with Large Vector Autoregressions
Provides fast and efficient procedures for Bayesian estimation and forecasting using state-of-the-art Vector Autoregressions. This package includes the model proposed by Shang, Wang, Woźniak (2026) <doi:10.48550/arXiv.2608.28087> and Chan (2020) <doi:10.1080/07350015.2018.1451336>, that is, a Bayesian Vector Autoregression with Minnesota priors and a flexible structure of the error term specification. The latter includes: conditional multivariate normal or Student’s t distributions, as well as homoskedastic or heteroskedastic specifications with a common volatility modelled by centred or non-centred Stochastic Volatility. Additionally, the package facilitates predictive analyses using density forecasting and forecast-error variance decompositions. All this is complemented by simple workflows, useful plots and summary functions, and comprehensive documentation. The 'bvars' package aligns with R packages 'bsvars' by Woźniak (2026) <doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by Wang & Woźniak (2026) <doi:10.32614/CRAN.package.bsvarSIGNs>, and 'bpvars' by Woźniak (2026) <doi:10.32614/CRAN.package.bpvars> regarding objects, workflows, and code structure, and they constitute an integrated toolset.
| Version: |
1.1 |
| Depends: |
R (≥ 4.1.0), RcppArmadillo, bsvars |
| Imports: |
generics, Rcpp (≥ 1.0.14), RcppProgress, RcppTN, R6 |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppProgress, RcppTN, bsvars |
| Suggests: |
tinytest |
| Published: |
2026-09-07 |
| DOI: |
10.32614/CRAN.package.bvars |
| Author: |
Rui Liu  [aut],
Andrés Ramirez Hassan
[aut],
Tomasz Woźniak
[aut, cre],
Fei Shang [ctb]
(co-authored the paper by Shang, Wang, Woźniak (2026), introduced
major code corrections and improvements),
Xiaolei Wang
[ctb] (co-authored the paper by Shang, Wang, Woźniak (2026)) |
| Maintainer: |
Tomasz Woźniak <wozniak.tom at pm.me> |
| BugReports: |
https://github.com/bsvars/bvars/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://bsvars.org/bvars/ |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
bvars results |
Documentation:
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