include estimate method

NAMESPACE

@@ -1,6 +1,15 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(estimate,BSVARSIGN)
+S3method(estimate,PosteriorBSVARSIGN)
+export(specify_bsvarSIGN)
+export(specify_data_matrices)
+export(specify_identification_bsvarSIGNs)
+export(specify_posterior_bsvarSIGN)
+export(specify_prior_bsvarSIGN)
+export(specify_starting_values_bsvarSIGN)
 import(RcppArmadillo)
+import(RcppProgress)
 import(bsvars)
 importFrom(R6,R6Class)
 importFrom(Rcpp,sourceCpp)

R/estimate.BSVARSIGN.R

@@ -0,0 +1,106 @@
+
+#' @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 (2021)
+#' 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. 
+#' 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$new()} function.
+#' @param S a positive integer, the number of posterior draws to be generated
+#' @param thin a positive integer, specifying the frequency of MCMC output thinning
+#' @param show_progress a logical value, if \code{TRUE} the estimation progress bar is visible
+#' 
+#' @return An object of class PosteriorBSVAR containing the Bayesian estimation output and containing two elements:
+#' 
+#'  \code{posterior} a list with a collection of \code{S} draws from the posterior distribution generated via Gibbs sampler containing:
+#'  \describe{
+#'  \item{A}{an \code{NxKxS} array with the posterior draws for matrix \eqn{A}}
+#'  \item{B}{an \code{NxNxS} array with the posterior draws for matrix \eqn{B}}
+#'  \item{hyper}{a \code{5xS} matrix with the posterior draws for the hyper-parameters of the hierarchical prior distribution}
+#' }
+#' 
+#' \code{last_draw} an object of class BSVAR with the last draw of the current MCMC run as the starting value to be passed to the continuation of the MCMC estimation using \code{estimate()}. 
+#'
+#' @author Tomasz Woźniak \email{wozniak.tom@pm.me}
+#' 
+#' @references Sampling from the generalised-normal full conditional posterior distribution of matrix \eqn{B} is implemented using the Gibbs sampler by:
+#' 
+#' Waggoner, D.F., and Zha, T., (2003) A Gibbs sampler for structural vector autoregressions. \emph{Journal of Economic Dynamics and Control}, \bold{28}, 349--366, \doi{https://doi.org/10.1016/S0165-1889(02)00168-9}.
+#'
+#' Sampling from the multivariate normal full conditional posterior distribution of each of the \eqn{A} matrix row is implemented using the sampler by:
+#' 
+#' Chan, J.C.C., Koop, G, and Yu, X. (2021) Large Order-Invariant Bayesian VARs with Stochastic Volatility.
+#' 
+#' @method estimate BSVARSIGN
+#' 
+#' 
+#' @export
+estimate.BSVARSIGN <- function(specification, S, thin = 10, show_progress = TRUE) {
+
+  # get the inputs to estimation
+  prior               = specification$prior$get_prior()
+  starting_values     = specification$starting_values$get_starting_values()
+  VB                  = specification$identification$get_identification()
+  data_matrices       = specification$data_matrices$get_data_matrices()
+
+  # estimation
+  qqq                 = .Call(`_bsvarSIGNs_bsvar_sign_cpp`, S, data_matrices$Y, data_matrices$X, VB, prior, starting_values, thin, show_progress)
+
+  specification$starting_values$set_starting_values(qqq$last_draw)
+  output              = specify_posterior_bsvarSIGN$new(specification, qqq$posterior)
+
+  # normalise output
+  BB                  = qqq$last_draw$B
+  BB                  = diag(sign(diag(BB))) %*% BB
+  normalise_posterior(output, BB)
+
+  return(output)
+}
+
+
+#' @inherit estimate.BSVARSIGN
+#' 
+#' @method estimate PosteriorBSVARSIGN
+#' 
+#' @param specification an object of class PosteriorBSVARSIGN generated using the \code{estimate.BSVARSIGN()} function.
+#' This setup facilitates the continuation of the MCMC sampling starting from the last draw of the previous run.
+#' 
+
+#' @export
+estimate.PosteriorBSVARSIGN <- function(specification, S, thin = 10, 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(`_bsvarSIGNs_bsvar_sign_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_bsvarSIGN$new(specification$last_draw, qqq$posterior)
+
+  # normalise output
+  BB                  = qqq$last_draw$B
+  BB                  = diag(sign(diag(BB))) %*% BB
+  normalise_posterior(output, BB)
+
+  return(output)
+}