diff --git a/DESCRIPTION b/DESCRIPTION index 7ee1820..12068dd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -5,7 +5,7 @@ Version: 1.0.1 Date: 2024-08-10 Authors@R: c(person("Xiaolei", "Wang", , "adamwang15@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0009-0005-6192-9061")),person("Tomasz", "Woźniak", , "wozniak.tom@pm.me", role = c("aut"), comment = c(ORCID = "0000-0003-2212-2378"))) Maintainer: Xiaolei Wang -Description: 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) . 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. +Description: 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) . 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. License: GPL (>= 3) Imports: Rcpp (>= 1.0.12), diff --git a/R/bsvarSIGNs-package.R b/R/bsvarSIGNs-package.R index 0cc6442..c102309 100644 --- a/R/bsvarSIGNs-package.R +++ b/R/bsvarSIGNs-package.R @@ -43,7 +43,8 @@ #' 'bsvarSIGNs' package is aligned regarding objects, workflows, and code #' structure with the R package 'bsvars' by #' Woźniak (2024) , and they constitute an -#' integrated toolset. +#' integrated toolset. It was granted the Di Cook Open-Source Statistical +#' Software Award by the Statistical Society of Australia in 2024. #' #' @details #' diff --git a/README.Rmd b/README.Rmd index 2995583..8f73adc 100644 --- a/README.Rmd +++ b/README.Rmd @@ -18,12 +18,14 @@ knitr::opts_chunk$set( An **R** package for Bayesian Estimation of Structural Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions +*The First Prize laureate of the Di Cook Open-Source Statistical Software Award granted by the Statistical Society of Australia in 2024* + [![](https://www.r-pkg.org/badges/version/bsvarSIGNs)](https://cran.r-project.org/package=bsvarSIGNs) [![R-CMD-check](https://github.com/bsvars/bsvarSIGNs/actions/workflows/R-CMD-check.yaml/badge.svg)](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 and the dummy observation priors 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. 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. +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 and the dummy observation priors 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. 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. diff --git a/README.md b/README.md index 1aa9f99..f99351c 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,9 @@ An **R** package for Bayesian Estimation of Structural Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions +*The First Prize laureate of the Di Cook Open-Source Statistical +Software Award granted by the Statistical Society of Australia in 2024* + [![](https://www.r-pkg.org/badges/version/bsvarSIGNs)](https://cran.r-project.org/package=bsvarSIGNs) @@ -36,7 +39,9 @@ comprehensive documentation. 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. +constitute an integrated toolset. It was granted the Di Cook Open-Source +Statistical Software Award by the Statistical Society of Australia in +2024. diff --git a/man/bsvarSIGNs-package.Rd b/man/bsvarSIGNs-package.Rd index 145d120..947ac2d 100644 --- a/man/bsvarSIGNs-package.Rd +++ b/man/bsvarSIGNs-package.Rd @@ -27,7 +27,8 @@ 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. +integrated toolset. It was granted the Di Cook Open-Source Statistical +Software Award by the Statistical Society of Australia in 2024. } \details{ \strong{Models.} All the SVAR models in this package are specified by two