R/estimate.BSVARSV.R

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

#' @title Bayesian estimation of a Structural Vector Autoregression with 
#' Stochastic Volatility heteroskedasticity via Gibbs sampler
#'
#' @description Estimates the SVAR with Stochastic Volatility (SV) heteroskedasticity 
#' proposed by Lütkepohl, Shang, Uzeda, and Woźniak (2025).
#' Implements 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 thanks to a hierarchical prior distribution. 
#' The SV model is estimated using a range of techniques including: 
#' simulation smoother, auxiliary mixture, ancillarity-sufficiency interweaving strategy, 
#' and generalised inverse Gaussian distribution summarised by Kastner & Frühwirth-Schnatter (2014). 
#' See section \bold{Details} for the model equations.
#' 
#' @details 
#' The heteroskedastic 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, \eqn{U}, are temporally and contemporaneously independent and jointly distributed with zero mean.
#' The structural shocks can be either normally or Student-t distributed, where in 
#' the latter case the shock-specific degrees of freedom parameters are estimated.
#' 
#' Two alternative specifications of the conditional variance of the \code{n}th shock at time \code{t} 
#' can be estimated: non-centred Stochastic Volatility by Lütkepohl, Shang, Uzeda, and Woźniak (2022) 
#' or centred Stochastic Volatility by Chan, Koop, & Yu (2021).
#' 
#' The non-centred Stochastic Volatility by Lütkepohl, Shang, Uzeda, and Woźniak (2022) 
#' is selected by setting argument \code{centred_sv} of function \code{specify_bsvar_sv$new()} to value \code{FALSE}.
#' It has the conditional variances given by:
#' \deqn{Var_{t-1}[u_{n.t}] = exp(w_n h_{n.t})}
#' where \eqn{w_n} is the estimated conditional standard deviation of the log-conditional variance
#' and the log-volatility process \eqn{h_{n.t}} follows an autoregressive process:
#' \deqn{h_{n.t} = g_n h_{n.t-1} + v_{n.t}}
#' where \eqn{h_{n.0}=0}, \eqn{g_n} is an autoregressive parameter and \eqn{v_{n.t}} is a standard normal error term.
#' 
#' The centred Stochastic Volatility by Chan, Koop, & Yu (2021)
#' is selected by setting argument \code{centred_sv} of function \code{specify_bsvar_sv$new()} to value \code{TRUE}.
#' Its conditional variances are given by:
#' \deqn{Var_{t-1}[u_{n.t}] = exp(h_{n.t})}
#' where the log-conditional variances \eqn{h_{n.t}} follow an autoregressive process:
#' \deqn{h_{n.t} = g_n h_{n.t-1} + v_{n.t}}
#' where \eqn{h_{n.0}=0}, \eqn{g_n} is an autoregressive parameter and \eqn{v_{n.t}} is a zero-mean normal error term
#' with variance \eqn{s_{v.n}^2}.
#' 
#' @param specification an object of class BSVARSV generated using the \code{specify_bsvar_sv$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 PosteriorBSVARSV 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}
#'  \item{h}{an \code{NxTxS} array with the posterior draws of the log-volatility processes}
#'  \item{rho}{an \code{NxS} matrix with the posterior draws of SV autoregressive parameters}
#'  \item{omega}{an \code{NxS} matrix with the posterior draws of SV process conditional standard deviations}
#'  \item{S}{an \code{NxTxS} array with the posterior draws of the auxiliary mixture component indicators}
#'  \item{sigma2_omega}{an \code{NxS} matrix with the posterior draws of the variances of the zero-mean normal prior for \code{omega}}
#'  \item{s_}{an \code{S}-vector with the posterior draws of the scale of the gamma prior of the hierarchical prior for \code{sigma2_omega}}
#' }
#' 
#' \code{last_draw} an object of class BSVARSV 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_sv}}, \code{\link{specify_posterior_bsvar_sv}}, \code{\link{normalise}}
#'
#' @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}.
#' 
#' Kastner, G. and Frühwirth-Schnatter, S. (2014) Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC 
#' Estimation of Stochastic Volatility Models. \emph{Computational Statistics & Data Analysis}, \bold{76}, 408--423, 
#' \doi{10.1016/j.csda.2013.01.002}.
#' 
#' Lütkepohl, H., Shang, F., Uzeda, L., and Woźniak, T. (2025) 
#' Partial identification of structural vector autoregressions with non-centred stochastic volatility. 
#' \emph{Journal of Econometrics}, 1--18, \doi{10.1016/j.jeconom.2025.106107}.
#' 
#' 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 BSVARSV
#' 
#' @examples
#' # simple workflow
#' ############################################################
#' specification  = specify_bsvar_sv$new(us_fiscal_lsuw)
#' burn_in        = estimate(specification, 5)
#' posterior      = estimate(burn_in, 5)
#' 
#' # workflow with the pipe |>
#' ############################################################
#' us_fiscal_lsuw |>
#'   specify_bsvar_sv$new() |>
#'   estimate(S = 5) |> 
#'   estimate(S = 5) -> post
#' 
#' @export
estimate.BSVARSV <- 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$identification$VB
  VA                  = specification$identification$VA
  data_matrices       = specification$data_matrices$get_data_matrices()
  centred_sv          = specification$centred_sv
  normal              = specification$get_normal()
  
  # estimation
  qqq                 = .Call(`_bsvars_bsvar_sv_cpp`, S, data_matrices$Y, data_matrices$X, prior, VB, VA, starting_values, normal, thin, centred_sv, show_progress)
  
  specification$starting_values$set_starting_values(qqq$last_draw)
  output              = specify_posterior_bsvar_sv$new(specification, qqq$posterior)
  
  # normalise output
  output              = normalise(output)
  
  return(output)
}





#' @inherit estimate.BSVARSV
#' 
#' @method estimate PosteriorBSVARSV
#' 
#' @param specification an object of class PosteriorBSVARSV 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
#' ############################################################
#' specification  = specify_bsvar_sv$new(us_fiscal_lsuw)
#' burn_in        = estimate(specification, 5)
#' posterior      = estimate(burn_in, 5)
#' 
#' # workflow with the pipe |>
#' ############################################################
#' us_fiscal_lsuw |>
#'   specify_bsvar_sv$new() |>
#'   estimate(S = 5) |> 
#'   estimate(S = 5) -> post
#' 
#' @export
estimate.PosteriorBSVARSV <- 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$VB
  VA                  = specification$last_draw$identification$VA
  data_matrices       = specification$last_draw$data_matrices$get_data_matrices()
  centred_sv          = specification$last_draw$centred_sv
  normal              = specification$last_draw$get_normal()
  
  # estimation
  qqq                 = .Call(`_bsvars_bsvar_sv_cpp`, S, data_matrices$Y, data_matrices$X, prior, VB, VA, starting_values, normal, thin, centred_sv, show_progress)
  
  specification$last_draw$starting_values$set_starting_values(qqq$last_draw)
  output              = specify_posterior_bsvar_sv$new(specification$last_draw, qqq$posterior)
  
  # normalise output
  output              = normalise(output)
  
  return(output)
}