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bsvars - Bayesian Estimation of Structural Vector Autoregressive Models

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, and include exclusion restrictions on autoregressive parameters. 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) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2025) <doi:10.1016/j.jeconom.2025.106107>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174> and they embed many popular models proposed by other authors. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R packages 'bsvarSIGNs' by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, 'bvars' by Liu, Ramirez Hassan, Woźniak (2026) <doi:10.32614/CRAN.package.bvars>, and 'bpvars' by Woźniak (2026) <doi:10.32614/CRAN.package.bpvars>, and they constitute an integrated toolset.

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bayesian-inferenceeconometricsvector-autoregressionopenblascppopenmp

9.24 score 63 stars 3 dependents 129 scripts 967 downloads

bpvars - Forecasting with Bayesian Panel Vector Autoregressions

Provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions following the specification by Sanchez-Martinez & Woźniak (2026) <doi:10.48550/arXiv.2606.14143>. The models include country-specific Vector Autoregressions (VARs) that share a global prior distribution that extend the model by Jarociński (2010) <doi: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) <doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and 'bvars' by Liu, Ramirez Hassan, & Woźniak (2026) <doi:10.32614/CRAN.package.bvars> and they constitute an integrated toolset. Copyright: 2025 International Labour Organization. The International Labour Organization should not be held responsible for any issues arising from the use of the 'bpvars' package or from the results obtained with it.

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bayesianbsvarsbvarpanelsdynamic-panel-dataforecastinghierarchical-modelsvarsopenblascppopenmp

5.38 score 6 stars 2 scripts 343 downloads

StealLikeBayes - A Compendium of Bayesian Statistical Routines Written in 'C++'

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.

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openblascppopenmp

5.05 score 9 stars 400 downloads

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.

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bvarcommon-stochastic-volatilityminnesota-priorstudent-t-errorsopenblascppopenmp

4.73 score 6 stars 6 scripts 580 downloads