bsvars
We develop R packages for Bayesian Structural Vector Autoregressions using frontier econometric methods and compiled code written in cpp.
bsvars
An R package for 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. 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 (2024) complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024), Lütkepohl & Woźniak (2020), and Song & Woźniak (2021). The bsvars package is aligned regarding objects, workflows, and code structure with the R package bsvarSIGNs by Wang & Woźniak (2024), and they constitute an integrated toolset.
[CRAN] [website] [vignette] [repo]
Presentations
[Forecasting for Social Good youtube recording]
[Forecasting for Social Good 2024-12 featuring bsvars 3.2 and bsvarSIGNs 1.0.1]
[Bayesian Econometrics students 2024-10 featuring bsvars 3.1]
[QuantEcon 2024-08 featuring bsvars 3.1 and bsvarSIGNs 1.0.1]
[Monash University 2024-08 featuring bsvars 3.1 and bsvarSIGNs 1.0]
[Workshops for Ukraine 2024-08 featuring bsvars 3.1]
[Macroeconometrics students 2024-05 featuring bsvars 2.1.0]
[Bayesian Econometrics students 2023-08 featuring bsvars 1.0.0]
bsvarSIGNs
An R package for Bayesian Estimation of Structural Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions
by Xiaolei Wang and Tomasz Woźniak
The First Prize laureate of the Di Cook Open-Source Statistical Software Award granted by the Statistical Society of Australia in 2024
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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). The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010), while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018). 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). 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. The bsvarSIGNs package is aligned regarding objects, workflows, and code structure with the R package bsvars by Woźniak (2024), 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.
Presentations
[Forecasting for Social Good youtube recording]
[Forecasting for Social Good 2024-12 featuring bsvars 3.2 and bsvarSIGNs 1.0.1]
[Bayesian Econometrics students 2024-10 featuring bsvarSIGNs 1.0.1]
[QuantEcon 2024-08 featuring bsvars 3.1 and bsvarSIGNs 1.0.1]
[Monash University 2024-08 featuring bsvars 3.1 and bsvarSIGNs 1.0]
bsvarTVPs
An R package for Bayesian Estimation of Heteroskedastic Structural Vector Autoregressions with Markov-Switching and Time-Varying Identification of the Structural Matrix
by Tomasz Woźniak and Annika Camehl
Efficient algorithms for Bayesian estimation of Structural Vector Autoregressions 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 (2024) Time-Varying Identification of Monetary Policy Shocks.
bvarPANELs
An R package for Forecasting with Bayesian Hierarchical Panel Vector Autoregressions
by Tomasz Woźniak and Miguel Sanchez-Martinez
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Provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Hierarchical Panel Vector Autoregressions (VARs). The model includes country-specific VARs that share a global prior distribution. 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. Beautiful plots, informative summary functions, and extensive documentation complement all this. An extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in C++. The bvarPANELs package is aligned regarding objects, workflows, and code structure with the R packages bsvars by Woźniak (2024) and bsvarSIGNs by Wang & Woźniak (2024), and they constitute an integrated toolset. Copyright: 2024 International Labour Organization.