Nothing
R/bsvarSIGNs-package.R
In bsvarSIGNs: Bayesian SVARs with Sign, Zero, and Narrative Restrictions
# #####################################################################################
# R package bsvarSIGNs by Xiaolei Wang Tomasz Woźniak Copyright (C) 2024
#
# This file is part of the R package bsvarSIGNs: Bayesian Estimation of Structural
# Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions
#
# The R package bsvarSIGNs is free software: you can redistribute it
# and/or modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation, either version 3 or
# any later version of the License.
#
# The R package bsvarSIGNs is distributed in the hope that it will be
# useful, but WITHOUT ANY WARRANTY; without even the implied warranty
# of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the R package bsvarSIGNs. If that is not the case, please
# refer to <http://www.gnu.org/licenses/>.
# #####################################################################################
#
#' @title Bayesian Estimation of Structural Vector Autoregressions Identified
#' by Sign, Zero, and Narrative Restrictions
#'
#' @description 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) <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. 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
#' equations, including the reduced form equation:
#' \deqn{y_t = Ax_t + \epsilon_t}
#' where \eqn{y_t} is an \code{N}-vector of dependent variables,
#' \eqn{x_t} is a \code{K}-vector of explanatory variables,
#' \eqn{\epsilon_t} is an \code{N}-vector of reduced form error terms,
#' and \eqn{A} is an \code{NxK} matrix of autoregressive slope coefficients and
#' parameters on deterministic terms in \eqn{x_t}.
#'
#' The structural equation is given by:
#' \deqn{B\epsilon_t = u_t}
#' where \eqn{u_t} is an \code{N}-vector of structural shocks, and
#' \eqn{B} is an \code{NxN} matrix of contemporaneous relationships.
#'
#' Finally, all of the models share the following assumptions regarding the
#' structural shocks \code{u_t}, namely, joint conditional normality given the
#' past observations collected in matrix \code{x_t}, and temporal and
#' contemporaneous independence. The latter implies zero correlations and
#' autocorrelations.
#'
#' \strong{Identification.} The identification of the SVAR model is achieved by
#' imposing:
#' \itemize{
#' \item sign restrictions on the structural matrix \eqn{B},
#' \item sign and zero restrictions on the zero-horizon impulse responses \eqn{\Theta_0 = B^{-1}},
#' \item sign restrictions on the impulse responses at other horizons \eqn{\Theta_i} for \eqn{i = 1, 2, \ldots},
#' \item sign restrictions on selected structural shocks \eqn{u_t},
#' \item two types of sign restrictions on the historical decompositions.
#' }
#' These restrictions determine the sampling algorithms of the structural matrix
#' \eqn{B} defined as
#' \deqn{B = Q'L}
#' where \eqn{Q} is an \code{NxN} orthogonal matrix and \eqn{L} is a lower-triangular
#' matrix \eqn{L = chol(\Sigma)^{-1}}, and \eqn{\Sigma} is the \code{NxN}
#' conditional covariance matrix of the reduced-form error term \eqn{\epsilon_t}.
#' Consult the original papers by Rubio-Ramírez, Waggoner & Zha (2010),
#' Arias, Rubio-Ramírez, & Waggoner (2018) and Antolín-Díaz and Rubio-Ramírez (2018)
#' for more details.
#'
#' \strong{Prior distributions.} All the models feature a hierarchical Minnesota
#' prior following the specification proposed by Giannone, Lenza, Primiceri (2015)
#' and featuring:
#' \itemize{
#' \item appropriate handling of unit-root non-stationary variables through
#' the prior mean of the autoregressive coefficients \eqn{A},
#' \item normal prior shrinkage exhibiting exponential decay in the lag order
#' of the autoregressive matrices,
#' \item sum-of-coefficients and dummy-initial-observation prior,
#' \item estimated shrinkage hyper-parameters,
#' \item inverse-Wishart prior for the reduced-form covariance matrix \eqn{\Sigma},
#' \item estimated diagonal elements of the inverse-Wishart prior scale matrix.
#' }
#'
#' @name bsvarSIGNs-package
#' @aliases bsvarSIGNs-package bsvarSIGNs
#' @useDynLib bsvarSIGNs, .registration = TRUE
#' @importFrom R6 R6Class
#' @importFrom Rcpp sourceCpp
#' @importFrom bsvars estimate forecast compute_impulse_responses compute_fitted_values compute_historical_decompositions compute_structural_shocks compute_variance_decompositions
#' @import bsvars
#' @import RcppArmadillo
#' @import RcppProgress
#'
#' @note This package is currently in active development. Your comments,
#' suggestions and requests are warmly welcome!
#'
#' @author Xiaolei Wang \email{adamwang15@gmail.com} & Tomasz Woźniak \email{wozniak.tom@pm.me}
#'
#' @references
#' Antolín-Díaz & Rubio-Ramírez (2018) Narrative Sign Restrictions for SVARs, American Economic Review, 108(10), 2802-29, <doi:10.1257/aer.20161852>.
#'
#' Arias, Rubio-Ramírez, & Waggoner (2018), Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications, Econometrica, 86(2), 685-720, <doi:10.3982/ECTA14468>.
#'
#' Giannone, Lenza, Primiceri (2015) Prior Selection for Vector Autoregressions, Review of Economics and Statistics, 97(2), 436-451 <doi:10.1162/REST_a_00483>.
#'
#' Rubio-Ramírez, Waggoner & Zha (2010) Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference, The Review of Economic Studies, 77(2), 665-696, <doi:10.1111/j.1467-937X.2009.00578.x>.
#'
#' Woźniak (2024) bsvars: Bayesian Estimation of Structural Vector Autoregressive Models. R package version 3.1, <doi:10.32614/CRAN.package.bsvars>.
#'
#' @examples
#' # investigate the effects of the optimism shock
#' data(optimism)
#'
#' # specify identifying restrictions:
#' # + no effect on productivity (zero restriction)
#' # + positive effect on stock prices (positive sign restriction)
#' sign_irf = matrix(c(0, 1, rep(NA, 23)), 5, 5)
#'
#' # specify the model
#' specification = specify_bsvarSIGN$new(optimism * 100,
#' p = 4,
#' sign_irf = sign_irf)
#'
#' # estimate the model
#' posterior = estimate(specification, S = 100)
#'
#' # compute and plot impulse responses
#' irf = compute_impulse_responses(posterior, horizon = 40)
#' plot(irf, probability = 0.68)
#'
NULL
Try the bsvarSIGNs package in your browser
Any scripts or data that you put into this service are public.
bsvarSIGNs documentation built on April 4, 2025, 12:30 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# #####################################################################################
# R package bsvarSIGNs by Xiaolei Wang Tomasz Woźniak Copyright (C) 2024
#
# This file is part of the R package bsvarSIGNs: Bayesian Estimation of Structural
# Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions
#
# The R package bsvarSIGNs is free software: you can redistribute it
# and/or modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation, either version 3 or
# any later version of the License.
#
# The R package bsvarSIGNs is distributed in the hope that it will be
# useful, but WITHOUT ANY WARRANTY; without even the implied warranty
# of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the R package bsvarSIGNs. If that is not the case, please
# refer to <http://www.gnu.org/licenses/>.
# #####################################################################################
#
#' @title Bayesian Estimation of Structural Vector Autoregressions Identified
#' by Sign, Zero, and Narrative Restrictions
#'
#' @description 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) <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. 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
#' equations, including the reduced form equation:
#' \deqn{y_t = Ax_t + \epsilon_t}
#' where \eqn{y_t} is an \code{N}-vector of dependent variables,
#' \eqn{x_t} is a \code{K}-vector of explanatory variables,
#' \eqn{\epsilon_t} is an \code{N}-vector of reduced form error terms,
#' and \eqn{A} is an \code{NxK} matrix of autoregressive slope coefficients and
#' parameters on deterministic terms in \eqn{x_t}.
#'
#' The structural equation is given by:
#' \deqn{B\epsilon_t = u_t}
#' where \eqn{u_t} is an \code{N}-vector of structural shocks, and
#' \eqn{B} is an \code{NxN} matrix of contemporaneous relationships.
#'
#' Finally, all of the models share the following assumptions regarding the
#' structural shocks \code{u_t}, namely, joint conditional normality given the
#' past observations collected in matrix \code{x_t}, and temporal and
#' contemporaneous independence. The latter implies zero correlations and
#' autocorrelations.
#'
#' \strong{Identification.} The identification of the SVAR model is achieved by
#' imposing:
#' \itemize{
#' \item sign restrictions on the structural matrix \eqn{B},
#' \item sign and zero restrictions on the zero-horizon impulse responses \eqn{\Theta_0 = B^{-1}},
#' \item sign restrictions on the impulse responses at other horizons \eqn{\Theta_i} for \eqn{i = 1, 2, \ldots},
#' \item sign restrictions on selected structural shocks \eqn{u_t},
#' \item two types of sign restrictions on the historical decompositions.
#' }
#' These restrictions determine the sampling algorithms of the structural matrix
#' \eqn{B} defined as
#' \deqn{B = Q'L}
#' where \eqn{Q} is an \code{NxN} orthogonal matrix and \eqn{L} is a lower-triangular
#' matrix \eqn{L = chol(\Sigma)^{-1}}, and \eqn{\Sigma} is the \code{NxN}
#' conditional covariance matrix of the reduced-form error term \eqn{\epsilon_t}.
#' Consult the original papers by Rubio-Ramírez, Waggoner & Zha (2010),
#' Arias, Rubio-Ramírez, & Waggoner (2018) and Antolín-Díaz and Rubio-Ramírez (2018)
#' for more details.
#'
#' \strong{Prior distributions.} All the models feature a hierarchical Minnesota
#' prior following the specification proposed by Giannone, Lenza, Primiceri (2015)
#' and featuring:
#' \itemize{
#' \item appropriate handling of unit-root non-stationary variables through
#' the prior mean of the autoregressive coefficients \eqn{A},
#' \item normal prior shrinkage exhibiting exponential decay in the lag order
#' of the autoregressive matrices,
#' \item sum-of-coefficients and dummy-initial-observation prior,
#' \item estimated shrinkage hyper-parameters,
#' \item inverse-Wishart prior for the reduced-form covariance matrix \eqn{\Sigma},
#' \item estimated diagonal elements of the inverse-Wishart prior scale matrix.
#' }
#'
#' @name bsvarSIGNs-package
#' @aliases bsvarSIGNs-package bsvarSIGNs
#' @useDynLib bsvarSIGNs, .registration = TRUE
#' @importFrom R6 R6Class
#' @importFrom Rcpp sourceCpp
#' @importFrom bsvars estimate forecast compute_impulse_responses compute_fitted_values compute_historical_decompositions compute_structural_shocks compute_variance_decompositions
#' @import bsvars
#' @import RcppArmadillo
#' @import RcppProgress
#'
#' @note This package is currently in active development. Your comments,
#' suggestions and requests are warmly welcome!
#'
#' @author Xiaolei Wang \email{adamwang15@gmail.com} & Tomasz Woźniak \email{wozniak.tom@pm.me}
#'
#' @references
#' Antolín-Díaz & Rubio-Ramírez (2018) Narrative Sign Restrictions for SVARs, American Economic Review, 108(10), 2802-29, <doi:10.1257/aer.20161852>.
#'
#' Arias, Rubio-Ramírez, & Waggoner (2018), Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications, Econometrica, 86(2), 685-720, <doi:10.3982/ECTA14468>.
#'
#' Giannone, Lenza, Primiceri (2015) Prior Selection for Vector Autoregressions, Review of Economics and Statistics, 97(2), 436-451 <doi:10.1162/REST_a_00483>.
#'
#' Rubio-Ramírez, Waggoner & Zha (2010) Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference, The Review of Economic Studies, 77(2), 665-696, <doi:10.1111/j.1467-937X.2009.00578.x>.
#'
#' Woźniak (2024) bsvars: Bayesian Estimation of Structural Vector Autoregressive Models. R package version 3.1, <doi:10.32614/CRAN.package.bsvars>.
#'
#' @examples
#' # investigate the effects of the optimism shock
#' data(optimism)
#'
#' # specify identifying restrictions:
#' # + no effect on productivity (zero restriction)
#' # + positive effect on stock prices (positive sign restriction)
#' sign_irf = matrix(c(0, 1, rep(NA, 23)), 5, 5)
#'
#' # specify the model
#' specification = specify_bsvarSIGN$new(optimism * 100,
#' p = 4,
#' sign_irf = sign_irf)
#'
#' # estimate the model
#' posterior = estimate(specification, S = 100)
#'
#' # compute and plot impulse responses
#' irf = compute_impulse_responses(posterior, horizon = 40)
#' plot(irf, probability = 0.68)
#'
NULL
Try the bsvarSIGNs package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.