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---
title: "bsvars"
---
We develop **R** packages for Bayesian Structural Vector Autoregressions using frontier econometric methods and compiled code written in **C++**.
<a href="mailto:contact@bsvars.org"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/envelope.svg" width="40" height="40"/> </a>
<a href="https://github.com/bsvars/bsvars"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/github.svg" width="40" height="40"/> </a>
<a href="https://bsky.app/profile/bsvars.org"> <img src="https://upload.wikimedia.org/wikipedia/commons/7/7a/Bluesky_Logo.svg" width="40" height="40"/> </a>
<a rel="me" href="https://fosstodon.org/@bsvars"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/mastodon.svg" width="40" height="40"/> </a>
# bsvars
<a href="https://bsvars.org/bsvars/"><img src="bsvars.png" align="right" height="139" alt="bsvarSIGNs website" /></a>
> An **R** package for Bayesian Estimation of Structural Vector Autoregressive Models
>
> by [Tomasz Woźniak](https://github.com/donotdespair)
>
> <a href="https://cran.r-project.org/package=bsvars"> <img src="https://www.r-pkg.org/badges/version/bsvars" width="76" height="20"/> </a>
<a href="https://cran.r-project.org/package=bsvars"> <img src="http://cranlogs.r-pkg.org/badges/grand-total/bsvars" width="108" height="20"/> </a> [](https://github.com/bsvars/bsvars/actions/workflows/R-CMD-check.yaml)
>
> Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by [Woźniak (2025)](http://doi.org/10.48550/arXiv.2410.15090) complement all this. The implemented techniques align closely with those presented in [Lütkepohl, Shang, Uzeda, & Woźniak (2025)](https://doi.org/10.1016/j.jeconom.2025.106107), [Lütkepohl & Woźniak (2020)](http://doi.org/10.1016/j.jedc.2020.103862), and [Song & Woźniak (2021)](http://doi.org/10.1093/acrefore/9780190625979.013.174). The **bsvars** package is aligned regarding objects, workflows, and code structure with the **R** package **bsvarSIGNs** by [Wang & Woźniak (2025)](http://doi.org/10.32614/CRAN.package.bsvarSIGNs), and they constitute an integrated toolset.
>
> [[CRAN]](https://cran.r-project.org/package=bsvars) [[website]](https://bsvars.org/bsvars/) [[vignette]](http://doi.org/10.48550/arXiv.2410.15090) [[manual]](https://cran.r-project.org/web/packages/bsvars/bsvars.pdf) [[resources]](https://bsvars.org/bsvars/#resources) [[repo]](https://github.com/bsvars/bsvars)\
>
> **Our Publications**\
> Lütkepohl, Shang, Uzeda, & Woźniak (2025) **Partial identification of structural vector autoregressions with non-centred stochastic volatility**, [Journal of Econometrics](https://doi.org/10.1016/j.jeconom.2025.106107)\
> Lütkepohl & Woźniak (2020) **Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity**, [Journal of Economic Dynamics and Control](http://doi.org/10.1016/j.jedc.2020.103862)\
> Song & Woźniak (2021) **Markov Switching**, [Oxford Research Encyclopedia of Economics and Finance](http://doi.org/10.1093/acrefore/9780190625979.013.174)
>
> **Youtube Recordings**\
> [[Forecasting for Social Good](https://www.f4sg.org/) [youtube recording](https://youtu.be/QT02OTZWW14)]\
> [[Workshops for Ukraine](https://sites.google.com/view/dariia-mykhailyshyna/main/r-workshops-for-ukraine) [youtube recording](https://www.youtube.com/watch?v=2iO0yrD0EtU)]\
>
> **Recent Presentations**\
> [[Szkoła Główna Handlowa](https://www.sgh.waw.pl/) [2024-12 featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-sgh/)]\
> [[Uniwersytet Warszawski](https://www.wne.uw.edu.pl/) [2024-12 featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-uwwne/)]\
> [[Uniwersytet Ekonomiczny w Krakowie](https://uek.krakow.pl/) [2024-12 featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-uek/)]\
> [[Forecasting for Social Good](https://www.f4sg.org/) [youtube recording](https://youtu.be/QT02OTZWW14)]\
> [[Forecasting for Social Good](https://www.f4sg.org/) [2024-12 featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-F4SG/)]\
> [[see all presentations]](https://bsvars.org/bsvars/#resources)\
# bsvarSIGNs
<a href="https://bsvars.org/bsvarSIGNs/"><img src="bsvarSIGNs.png" align="right" height="139" alt="bsvarSIGNs website" /></a>
> An **R** package for Bayesian Estimation of Structural Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions
>
> by [Xiaolei Wang](https://github.com/adamwang15) and [Tomasz Woźniak](https://github.com/donotdespair)
>
> *The First Prize laureate of the Di Cook Open-Source Statistical Software Award granted by the Statistical Society of Australia in 2024*
>
> <a href="https://cran.r-project.org/package=bsvarSIGNs"> <img src="https://www.r-pkg.org/badges/version/bsvarSIGNs" width="76" height="20"/> </a>
<a href="https://cran.r-project.org/package=bsvarSIGNs"> <img src="http://cranlogs.r-pkg.org/badges/grand-total/bsvarSIGNs" width="108" height="20"/> </a>
[](https://github.com/bsvars/bsvarSIGNs/actions/workflows/R-CMD-check.yaml).
>
> Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior as in [Giannone, Lenza, Primiceri (2015)](http://doi.org/10.1162/REST_a_00483). The sign restrictions are implemented employing the methods proposed by [Rubio-Ramírez, Waggoner & Zha (2010)](http://doi.org/10.1111/j.1467-937X.2009.00578.x), while identification through sign and zero restrictions follows the approach developed by [Arias, Rubio-Ramírez, & Waggoner (2018)](http://doi.org/10.3982/ECTA14468). Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by [Antolín-Díaz and Rubio-Ramírez (2018)](http://doi.org/10.1257/aer.20161852). Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by [Wang & Woźniak (2025)](https://doi.org/10.48550%2FarXiv.2501.16711). The **bsvarSIGNs** package is aligned regarding objects, workflows, and code structure with the **R** package **bsvars** by [Woźniak (2024)](http://doi.org/10.32614/CRAN.package.bsvars), and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.
>
> [[CRAN]](https://cran.r-project.org/package=bsvarSIGNs) [[website]](https://bsvars.org/bsvarSIGNs/) [[vignette]](https://doi.org/10.48550%2FarXiv.2501.16711) [[manual]](https://cran.r-project.org/web/packages/bsvarSIGNs/bsvarSIGNs.pdf) [[resources]](https://bsvars.org/bsvarSIGNs/#resources) [[repo]](https://github.com/bsvars/bsvarSIGNs)\
>
> **Youtube Recordings**\
> [[Workshops for Ukraine](https://sites.google.com/view/dariia-mykhailyshyna/main/r-workshops-for-ukraine) [youtube recording](https://www.youtube.com/watch?v=zjjM0l2r6gM)]\
> [[Forecasting for Social Good](https://www.f4sg.org/) [youtube recording](https://youtu.be/QT02OTZWW14)]\
>
> **Recent Presentations**\
> [[Quantlab](https://quantilab.github.io/) [2025-07 featuring **bsvarSIGNs** 2.0](https://bsvars.org/2025-07-28-QuantLab/)]\
> [[IIF Workshop on Open Source Forecasting](https://event.nectric.com.au/iif-osf/) [2025-06 featuring **bsvarSIGNs** 2.0](https://bsvars.org/2025-06-iifosf/)]\
> [[Workshops for Ukraine](https://sites.google.com/view/dariia-mykhailyshyna/main/r-workshops-for-ukraine) [2025-03 featuring **bsvarSIGNs** 2.0](https://bsvars.org/2025-03-bsvarSIGNs-w4UKR/)]\
> [[Statistical Society of Australia](https://statsoc.org.au/) [2025-02 featuring **bsvarSIGNs** 2.0](https://bsvars.org/2025-02-DiCook/)]\
> [[Szkoła Główna Handlowa](https://www.sgh.waw.pl/) [2024-12 featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-sgh/)]\
> [[Uniwersytet Warszawski](https://www.wne.uw.edu.pl/) [2024-12 featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-uwwne/)]\
> [[Uniwersytet Ekonomiczny w Krakowie](https://uek.krakow.pl/) [2024-12 featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-uek/)]\
> [[Forecasting for Social Good](https://www.f4sg.org/) [youtube recording](https://youtu.be/QT02OTZWW14)]\
> [[Forecasting for Social Good](https://www.f4sg.org/) [2024-12 featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-F4SG/)]\
> [[see all presentations]](https://bsvars.org/bsvarSIGNs/#resources)\
# bpvars
<a href="https://bsvars.org/bpvars/"><img src="bpvars.png" align="right" height="139" alt="bpvars website" /></a>
> An **R** package for Forecasting with Bayesian Panel Vector Autoregressions
>
> by [Tomasz Woźniak](https://github.com/donotdespair) with contributions by [Miguel Sanchez Martinez](https://researchrepository.ilo.org/esploro/profile/miguel_sanchez_martinez)
>
> *The package is designed for forecasting labour market outcomes at the International Labour Organization.*
>
> <a href="https://cran.r-project.org/package=bpvars"> <img src="https://www.r-pkg.org/badges/version/bpvars" width="76" height="20"/> </a>
<a href="https://cran.r-project.org/package=bpvars"> <img src="http://cranlogs.r-pkg.org/badges/grand-total/bpvars" width="108" height="20"/> </a>
[](https://github.com/bsvars/bpvars/actions/workflows/R-CMD-check.yaml).
>
> Provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions. The models include country-specific Vector Autoregressions (VARs) that share a global prior distribution that extend the model by [Jarociński (2010)](http://doi.org/10.1002/jae.1082). Under this prior expected value, each country's system follows a global VAR with country-invariant parameters. Further flexibility is provided by the hierarchical prior structure that retains the Minnesota prior interpretation for the global VAR and features estimated prior covariance matrices, shrinkage, and persistence levels. Bayesian forecasting is developed for models including exogenous variables, allowing conditional forecasts given the future trajectories of some variables and restricted forecasts assuring that rates are forecasted to stay positive and less than 100. The package implements the model specification, estimation, and forecasting routines, facilitating coherent workflows and reproducibility. It also includes automated pseudo-out-of-sample forecasting and computation of forecasting performance measures. Beautiful plots, informative summary functions, and extensive documentation complement all this. Extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in **C++**. The **bpvars** package is aligned regarding objects, workflows, and code structure with the **R** packages **bsvars** by [Woźniak (2024)](http://doi.org/10.32614/CRAN.package.bsvars) and **bsvarSIGNs** by [Wang & Woźniak (2025)](http://doi.org/10.32614/CRAN.package.bsvarSIGNs), and they constitute an integrated toolset. Copyright: 2025 International Labour Organization.
>
> [[CRAN]](https://cran.r-project.org/package=bpvars) [[website]](https://bsvars.org/bpvars/) [[manual]](https://cloud.r-project.org/web/packages/bpvars/refman/bpvars.html) [[repo]](https://github.com/bsvars/bpvars) [[resources]](https://bsvars.org/bpvars/#resources)
>
> **Youtube Recordings**\
> [[International Labour Organization 2026-03](https://www.ilo.org/meetings-and-events/forecasting-labour-market-outcomes-using-bayesian-hierarchical-panel-vars) [youtube recording](https://www.youtube.com/watch?v=ef3eXbqNbr8)]\
>
> **Presentations**\
> [[International Labour Organization](https://www.ilo.org/) [2026-03 featuring **bpvars** 1.0](https://bsvars.org/2026-03-bpvars-ilo/)]\
> [[International Labour Organization](https://www.ilo.org/) [2025-03 featuring **bvarPANELs** 0.2](https://bsvars.org/2025-03-bvarPANELs-ilo/)]\
# StealLikeBayes
<a href="https://bsvars.org/StealLikeBayes/"><img src="StealLikeBayes.png" align="right" height="139" alt="StealLikeBayes website" /></a>
> A Compendium of Bayesian Statistical Routines Written in **C++**
>
> by [Tomasz Woźniak](https://github.com/donotdespair), [Xiaolei Wang](https://github.com/adamwang15), [Longcan Li](https://github.com/Longcando), [Shelly Xie](https://github.com/shellyxie1), [Filip Reierson](https://github.com/freierson) [Kenyon Ng](https://github.com/weiyaw)
>
> <a href="https://cran.r-project.org/package=StealLikeBayes"> <img src="https://www.r-pkg.org/badges/version/StealLikeBayes" width="76" height="20"/> </a>
[](https://github.com/bsvars/StealLikeBayes/actions/workflows/R-CMD-check.yaml)
>
> This is a compendium of **C++** routines useful for Bayesian statistics. We steal other people's **C++** code, repurpose it, and export it so developers of **R** packages can use it in their **C++** code. We actually don't steal anything, or claim that Thomas Bayes did, but copy code that is compatible with our GPL 3 licence, fully acknowledging the authorship of the original code.
>
> [[CRAN]](https://cran.r-project.org/package=StealLikeBayes) [[website]](https://bsvars.org/StealLikeBayes/) [[manual]](https://cran.r-project.org/web/packages/StealLikeBayes/StealLikeBayes.pdf) [[repo]](https://github.com/bsvars/StealLikeBayes)
<!-- # bsvarTVPs -->
<!-- > An **R** package for Bayesian Structural VARs with Time-Varying Identification -->
<!-- > -->
<!-- > by [Tomasz Woźniak](https://github.com/donotdespair) and [Annika Camehl](https://github.com/aschnuecker) -->
<!-- > -->
<!-- > Efficient algorithms for Bayesian estimation of Structural Vector Autoregressions (VARs) with Stochastic Volatility heteroskedasticity, Markov-switching and Time-Varying Identification of the Structural Matrix, and a three-level global-local hierarchical prior shrinkage for the structural and autoregressive matrices. The models were developed for a paper by [Camehl & Woźniak (2025)](https://doi.org/10.48550/arXiv.2502.19659) titled *Time-Varying Identification of Structural Vector Autoregressions*. The 'bsvarTVPs' package is aligned regarding objects, workflows, and code structure with the R packages 'bsvars' by [Woźniak (2024)](https://doi.org/10.32614/CRAN.package.bsvars) and 'bsvarSIGNs' by [Wang & Woźniak (2025)](https://doi.org/10.32614/CRAN.package.bsvarSIGNs), and they constitute an integrated toolset. -->
<!-- > -->
<!-- > [[repo]](https://github.com/bsvars/bsvarTVPs) [[working paper]](https://doi.org/10.48550/arXiv.2502.19659) -->