diff --git a/DESCRIPTION b/DESCRIPTION index 82cf750..7ff23fc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: bvarPANELs Type: Package Title: Forecasting with Bayesian Hierarchical Panel Vector Autoregressions -Description: Forecasting a multicountry time series panel data using Bayesian Vector Autoregressions with a three-level country-global hierarchical prior structure. The model estimates country-specific autoregressive and covariance parameters as well as their global counterparts that play the role of the country-specific prior mean values in a Bayesian panel model. The package facilitates predictions for models with and without exogenous variables and including the possibility of conditional forecasts given the future projections of some of the variables. Beautiful plots, informative summary functions, and extensive documentation complement all this. Copyright: 2024 International Labour Organization. +Description: Provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Hierarchical Panel Vector Autoregressions. 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. Version: 0.0.1.9000 Date: 2024-09-01 Authors@R: c(person(given="Tomasz", family="Woźniak", email="wozniak.tom@pm.me", role = diff --git a/R/bvarPANELs-package.R b/R/bvarPANELs-package.R index cf4d965..39be125 100644 --- a/R/bvarPANELs-package.R +++ b/R/bvarPANELs-package.R @@ -22,9 +22,27 @@ # #' @title Forecasting with Bayesian Hierarchical Panel Vector Autoregressions #' -#' @description Forecasting a multi-country time series panel data using -#' Bayesian Vector Autoregressions with a three-level country-global -#' hierarchical prior structure. +#' @description Provides Bayesian estimation and forecasting of dynamic panel +#' data using Bayesian Hierarchical Panel Vector Autoregressions. 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) +#' \doi{10.32614/CRAN.package.bsvars} and 'bsvarSIGNs' by Wang & Woźniak (2024) +#' \doi{10.32614/CRAN.package.bsvarSIGNs}, and they constitute an integrated +#' toolset. Copyright: 2024 International Labour Organization. #' #' @details #' The package provides a set of functions for predictive analysis with the diff --git a/man/bvarPANELs-package.Rd b/man/bvarPANELs-package.Rd index e19c1ad..7814c6c 100644 --- a/man/bvarPANELs-package.Rd +++ b/man/bvarPANELs-package.Rd @@ -6,9 +6,27 @@ \alias{bvarPANELs} \title{Forecasting with Bayesian Hierarchical Panel Vector Autoregressions} \description{ -Forecasting a multi-country time series panel data using -Bayesian Vector Autoregressions with a three-level country-global -hierarchical prior structure. +Provides Bayesian estimation and forecasting of dynamic panel +data using Bayesian Hierarchical Panel Vector Autoregressions. 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) +\doi{10.32614/CRAN.package.bsvars} and 'bsvarSIGNs' by Wang & Woźniak (2024) +\doi{10.32614/CRAN.package.bsvarSIGNs}, and they constitute an integrated +toolset. Copyright: 2024 International Labour Organization. } \details{ The package provides a set of functions for predictive analysis with the diff --git a/man/estimate.BVARPANEL.Rd b/man/estimate.BVARPANEL.Rd index a1871ef..6348cbb 100644 --- a/man/estimate.BVARPANEL.Rd +++ b/man/estimate.BVARPANEL.Rd @@ -81,7 +81,7 @@ To obtain \code{S} draws from the posterior distribution: \item Repeat step 2. \code{S} times. Return \eqn{\{\theta_1^{(s)},\theta_2^{(s)}\}_{s=1}^{S}} as a sample drawn from the posterior distribution \eqn{p(\theta_1,\theta_2|\mathbf{Y})}. } -The \code{estimate()} function returns the draws from the posetrior distribution +The \code{estimate()} function returns the draws from the posterior distribution of the parameters of the hierarchical panel VAR model listed above. \strong{Thinning.} diff --git a/man/estimate.PosteriorBVARPANEL.Rd b/man/estimate.PosteriorBVARPANEL.Rd index f34acc3..6ee5b83 100644 --- a/man/estimate.PosteriorBVARPANEL.Rd +++ b/man/estimate.PosteriorBVARPANEL.Rd @@ -83,7 +83,7 @@ To obtain \code{S} draws from the posterior distribution: \item Repeat step 2. \code{S} times. Return \eqn{\{\theta_1^{(s)},\theta_2^{(s)}\}_{s=1}^{S}} as a sample drawn from the posterior distribution \eqn{p(\theta_1,\theta_2|\mathbf{Y})}. } -The \code{estimate()} function returns the draws from the posetrior distribution +The \code{estimate()} function returns the draws from the posterior distribution of the parameters of the hierarchical panel VAR model listed above. \strong{Thinning.}