Package: bsvarSIGNs 1.0.1

Xiaolei Wang

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>. 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 tool 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. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset.

Authors:Xiaolei Wang [aut, cre], Tomasz Woźniak [aut]

bsvarSIGNs_1.0.1.tar.gz
bsvarSIGNs_1.0.1.tar.gz(r-4.5-noble)bsvarSIGNs_1.0.1.tar.gz(r-4.4-noble)
bsvarSIGNs_1.0.1.tgz(r-4.4-emscripten)bsvarSIGNs_1.0.1.tgz(r-4.3-emscripten)
bsvarSIGNs.pdf |bsvarSIGNs.html
bsvarSIGNs/json (API)
NEWS

# Install 'bsvarSIGNs' in R:
install.packages('bsvarSIGNs', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/bsvars/bsvarsigns/issues

Pkgdown:https://bsvars.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • monetary - A 6-variable US monetary policy data, from 1965 Jan to 2007 Aug
  • optimism - A 5-variable US business cycle data, from 1955 Q1 to 2004 Q4

openblascppopenmp

2.00 score 9 scripts 309 downloads 5 exports 10 dependencies

Last updated 4 months agofrom:1ec2e580e0. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-linux-x86_64OKDec 04 2024

Exports:specify_bsvarSIGNspecify_identification_bsvarSIGNspecify_narrativespecify_posterior_bsvarSIGNspecify_prior_bsvarSIGN

Dependencies:bsvarscodaGIGrvglatticeR6RcppRcppArmadilloRcppProgressRcppTNstochvol