bsvarSIGNs - Bayesian SVARs with Sign, Zero, and Narrative Restrictions
Implements state-of-the-art algorithms for the Bayesian
analysis of Structural Vector Autoregressions (SVARs)
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 and the dummy
observation priors as in Giannone, Lenza, Primiceri (2015)
<doi:10.1162/REST_a_00483> extended by the COVID-specific
heteroskedasticity proposed by Lenza, Primiceri (2022)
<doi:10.1002/jae.2895>. The sign restrictions are implemented
employing the methods proposed by Rubio-Ramírez, Waggoner & Zha
(2010) <doi: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)
<doi:10.3982/ECTA14468>. Furthermore, our toolset provides
algorithms for identification via sign and narrative
restrictions, in line with the methods introduced by
Antolín-Díaz and Rubio-Ramírez (2018)
<doi: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 (2024)
<doi:10.48550/arXiv.2501.16711>. The 'bsvarSIGNs' package is
aligned regarding objects, workflows, and code structure with
the R packages 'bsvars' by Woźniak (2024)
<doi:10.32614/CRAN.package.bsvars>, '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. It was granted the
Di Cook Open-Source Statistical Software Award by the
Statistical Society of Australia in 2024.
