R/estimate.BSVAR.R

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

#' @title Bayesian estimation of a homoskedastic Structural Vector Autoregression via Gibbs sampler
#'
#' @description Estimates the homoskedastic SVAR using the Gibbs sampler proposed by Waggoner & Zha (2003)
#' for the structural matrix \eqn{B} and the equation-by-equation sampler by Chan, Koop, & Yu (2024)
#' for the autoregressive slope parameters \eqn{A}. Additionally, the parameter matrices \eqn{A} and \eqn{B}
#' follow a Minnesota prior and generalised-normal prior distributions respectively with the matrix-specific
#' overall shrinkage parameters estimated using a hierarchical prior distribution
#' as in Lütkepohl, Shang, Uzeda, and Woźniak (2024). See section \bold{Details} for the model equations.
#' 
#' @details 
#' The homoskedastic SVAR model is given by the reduced form equation:
#' \deqn{Y = AX + E}
#' where \eqn{Y} is an \code{NxT} matrix of dependent variables, \eqn{X} is a \code{KxT} matrix of explanatory variables, 
#' \eqn{E} is an \code{NxT} matrix 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}.
#' 
#' The structural equation is given by
#' \deqn{BE = U}
#' where \eqn{U} is an \code{NxT} matrix of structural form error terms, and
#' \eqn{B} is an \code{NxN} matrix of contemporaneous relationships.
#' 
#' Finally, the structural shocks, \code{U}, are temporally and contemporaneously independent and jointly normally distributed with zero mean and unit variances.
#' 
#' @param specification an object of class BSVAR generated using the \code{specify_bsvar$new()} function.
#' @param S a positive integer, the number of posterior draws to be generated
#' @param thin a positive integer, specifying the frequency of MCMC output thinning
#' @param show_progress a logical value, if \code{TRUE} the estimation progress bar is visible
#' 
#' @return An object of class PosteriorBSVAR containing the Bayesian estimation output and containing two elements:
#' 
#'  \code{posterior} a list with a collection of \code{S} draws from the posterior distribution generated via Gibbs sampler containing:
#'  \describe{
#'  \item{A}{an \code{NxKxS} array with the posterior draws for matrix \eqn{A}}
#'  \item{B}{an \code{NxNxS} array with the posterior draws for matrix \eqn{B}}
#'  \item{hyper}{a \code{5xS} matrix with the posterior draws for the hyper-parameters of the hierarchical prior distribution}
#' }
#' 
#' \code{last_draw} an object of class BSVAR with the last draw of the current MCMC run as the starting value to be passed to the continuation of the MCMC estimation using \code{estimate()}. 
#'
#' @seealso \code{\link{specify_bsvar}}, \code{\link{specify_posterior_bsvar}}, \code{\link{normalise_posterior}}
#'
#' @author Tomasz Woźniak \email{wozniak.tom@pm.me}
#' 
#' @references 
#' 
#' Chan, J.C.C., Koop, G, and Yu, X. (2024) Large Order-Invariant Bayesian VARs with Stochastic Volatility. \emph{Journal of Business & Economic Statistics}, \bold{42}, \doi{10.1080/07350015.2023.2252039}.
#' 
#' Lütkepohl, H., Shang, F., Uzeda, L., and Woźniak, T. (2024) Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference. \emph{University of Melbourne Working Paper}, 1--57, \doi{10.48550/arXiv.2404.11057}.
#' 
#' Waggoner, D.F., and Zha, T., (2003) A Gibbs sampler for structural vector autoregressions. \emph{Journal of Economic Dynamics and Control}, \bold{28}, 349--366, \doi{10.1016/S0165-1889(02)00168-9}.
#'
#' 
#' @method estimate BSVAR
#' 
#' @examples
#' # simple workflow
#' ############################################################
#' # upload data
#' data(us_fiscal_lsuw)
#' 
#' # specify the model and set seed
#' specification  = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#' 
#' # run the burn-in
#' burn_in        = estimate(specification, 5)
#' 
#' # estimate the model
#' posterior      = estimate(burn_in, 10, thin = 2)
#' 
#' # workflow with the pipe |>
#' ############################################################
#' set.seed(123)
#' us_fiscal_lsuw |>
#'   specify_bsvar$new(p = 1) |>
#'   estimate(S = 5) |> 
#'   estimate(S = 10, thin = 2) |> 
#'   compute_impulse_responses(horizon = 4) -> irf
#' 
#' @export
estimate.BSVAR <- function(specification, S, thin = 1, show_progress = TRUE) {
  
  # get the inputs to estimation
  prior               = specification$prior$get_prior()
  starting_values     = specification$starting_values$get_starting_values()
  VB                  = specification$identification$get_identification()
  data_matrices       = specification$data_matrices$get_data_matrices()

  # estimation
  qqq                 = .Call(`_bsvars_bsvar_cpp`, S, data_matrices$Y, data_matrices$X, VB, prior, starting_values, thin, show_progress)
  
  specification$starting_values$set_starting_values(qqq$last_draw)
  output              = specify_posterior_bsvar$new(specification, qqq$posterior)
   
  # normalise output
  BB                  = qqq$last_draw$B
  BB                  = diag(sign(diag(BB))) %*% BB
  normalise_posterior(output, BB)
    
  return(output)
}


#' @inherit estimate.BSVAR
#' 
#' @method estimate PosteriorBSVAR
#' 
#' @param specification an object of class PosteriorBSVAR generated using the \code{estimate.BSVAR()} function.
#' This setup facilitates the continuation of the MCMC sampling starting from the last draw of the previous run.
#' 
#' @examples
#' # simple workflow
#' ############################################################
#' # upload data
#' data(us_fiscal_lsuw)
#' 
#' # specify the model and set seed
#' specification  = specify_bsvar$new(us_fiscal_lsuw, p = 1)
#' set.seed(123)
#' 
#' # run the burn-in
#' burn_in        = estimate(specification, 5)
#' 
#' # estimate the model
#' posterior      = estimate(burn_in, 10, thin = 2)
#' 
#' # workflow with the pipe |>
#' ############################################################
#' set.seed(123)
#' us_fiscal_lsuw |>
#'   specify_bsvar$new(p = 1) |>
#'   estimate(S = 5) |> 
#'   estimate(S = 10, thin = 2) |> 
#'   compute_impulse_responses(horizon = 4) -> irf
#' 
#' @export
estimate.PosteriorBSVAR <- function(specification, S, thin = 1, show_progress = TRUE) {
  
  # get the inputs to estimation
  prior               = specification$last_draw$prior$get_prior()
  starting_values     = specification$last_draw$starting_values$get_starting_values()
  VB                  = specification$last_draw$identification$get_identification()
  data_matrices       = specification$last_draw$data_matrices$get_data_matrices()
  
  # estimation
  qqq                 = .Call(`_bsvars_bsvar_cpp`, S, data_matrices$Y, data_matrices$X, VB, prior, starting_values, thin, show_progress)
  
  specification$last_draw$starting_values$set_starting_values(qqq$last_draw)
  output              = specify_posterior_bsvar$new(specification$last_draw, qqq$posterior)
  
  # normalise output
  BB                  = qqq$last_draw$B
  BB                  = diag(sign(diag(BB))) %*% BB
  normalise_posterior(output, BB)
  
  return(output)
}