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 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 (2024) <doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and 'bpvars' by Woźniak (2025) <doi:10.32614/CRAN.package.bpvars> regarding objects, workflows, and code structure, and they constitute an integrated toolset.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling linux/jammy R-4.5 | bvars_1.0.tar.gz |
510.4 KiB |
1.0 |
rolling linux/noble R-4.5 | bvars_1.0.tar.gz |
521.1 KiB |
1.0 |
rolling source/ R- | bvars_1.0.tar.gz |
120.3 KiB |
1.0 |
latest linux/jammy R-4.5 | bvars_1.0.tar.gz |
510.4 KiB |
1.0 |
latest linux/noble R-4.5 | bvars_1.0.tar.gz |
521.1 KiB |
1.0 |
latest source/ R- | bvars_1.0.tar.gz |
120.3 KiB |
1.0 |
2026-04-23 source/ R- | bvars_1.0.tar.gz |
0 B |