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R/specify_bsvar.R
In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models
#' R6 Class Representing PriorBSVAR
#'
#' @description
#' The class PriorBSVAR presents a prior specification for the homoskedastic bsvar model.
#'
#' @examples
#' prior = specify_prior_bsvar$new(N = 3, p = 1) # a prior for 3-variable example with one lag
#' prior$A # show autoregressive prior mean
#'
#' @export
specify_prior_bsvar = R6::R6Class(
"PriorBSVAR",
public = list(
#' @field A an \code{NxK} matrix, the mean of the normal prior distribution for the parameter matrix \eqn{A}.
A = matrix(),
#' @field A_V_inv a \code{KxK} precision matrix of the normal prior distribution for each of
#' the row of the parameter matrix \eqn{A}. This precision matrix is equation invariant.
A_V_inv = matrix(),
#' @field B_V_inv an \code{NxN} precision matrix of the generalised-normal prior distribution
#' for the structural matrix \eqn{B}. This precision matrix is equation invariant.
B_V_inv = matrix(),
#' @field B_nu a positive integer greater of equal than \code{N}, a shape parameter of
#' the generalised-normal prior distribution for the structural matrix \eqn{B}.
B_nu = NA,
#' @field hyper_nu_B a positive scalar, the shape parameter of the inverted-gamma 2 prior
#' for the overall shrinkage parameter for matrix \eqn{B}.
hyper_nu_B = NA,
#' @field hyper_a_B a positive scalar, the shape parameter of the gamma prior
#' for the second-level hierarchy for the overall shrinkage parameter for matrix \eqn{B}.
hyper_a_B = NA,
#' @field hyper_s_BB a positive scalar, the scale parameter of the inverted-gamma 2 prior
#' for the third-level of hierarchy for overall shrinkage parameter for matrix \eqn{B}.
hyper_s_BB = NA,
#' @field hyper_nu_BB a positive scalar, the shape parameter of the inverted-gamma 2 prior
#' for the third-level of hierarchy for overall shrinkage parameter for matrix \eqn{B}.
hyper_nu_BB = NA,
#' @field hyper_nu_A a positive scalar, the shape parameter of the inverted-gamma 2 prior
#' for the overall shrinkage parameter for matrix \eqn{A}.
hyper_nu_A = NA,
#' @field hyper_a_A a positive scalar, the shape parameter of the gamma prior
#' for the second-level hierarchy for the overall shrinkage parameter for matrix \eqn{A}.
hyper_a_A = NA,
#' @field hyper_s_AA a positive scalar, the scale parameter of the inverted-gamma 2 prior
#' for the third-level of hierarchy for overall shrinkage parameter for matrix \eqn{A}.
hyper_s_AA = NA,
#' @field hyper_nu_AA a positive scalar, the shape parameter of the inverted-gamma 2 prior
#' for the third-level of hierarchy for overall shrinkage parameter for matrix \eqn{A}.
hyper_nu_AA = NA,
#' @description
#' Create a new prior specification PriorBSVAR.
#' @param N a positive integer - the number of dependent variables in the model.
#' @param p a positive integer - the autoregressive lag order of the SVAR model.
#' @param d a positive integer - the number of \code{exogenous} variables in the model.
#' @param stationary an \code{N} logical vector - its element set to \code{FALSE} sets
#' the prior mean for the autoregressive parameters of the \code{N}th equation to the white noise process,
#' otherwise to random walk.
#' @return A new prior specification PriorBSVAR.
#' @examples
#' # a prior for 3-variable example with one lag and stationary data
#' prior = specify_prior_bsvar$new(N = 3, p = 1, stationary = rep(TRUE, 3))
#' prior$A # show autoregressive prior mean
#'
initialize = function(N, p, d = 0, stationary = rep(FALSE, N)){
stopifnot("Argument N must be a positive integer number." = N > 0 & N %% 1 == 0)
stopifnot("Argument p must be a positive integer number." = p > 0 & p %% 1 == 0)
stopifnot("Argument d must be a non-negative integer number." = d >= 0 & d %% 1 == 0)
stopifnot("Argument stationary must be a logical vector of length N." = length(stationary) == N & is.logical(stationary))
K = N * p + 1 + d
self$A = cbind(diag(as.numeric(!stationary)), matrix(0, N, K - N))
self$A_V_inv = diag(c(kronecker((1:p)^2, rep(1, N) ), rep(1, d + 1)))
self$B_V_inv = diag(N)
self$B_nu = N
self$hyper_nu_B = 10
self$hyper_a_B = 10
self$hyper_s_BB = 100
self$hyper_nu_BB = 1
self$hyper_nu_A = 10
self$hyper_a_A = 10
self$hyper_s_AA = 10
self$hyper_nu_AA = 10
}, # END initialize
#' @description
#' Returns the elements of the prior specification PriorBSVAR as a \code{list}.
#'
#' @examples
#' # a prior for 3-variable example with four lags
#' prior = specify_prior_bsvar$new(N = 3, p = 4)
#' prior$get_prior() # show the prior as list
#'
get_prior = function(){
list(
A = self$A,
A_V_inv = self$A_V_inv,
B_V_inv = self$B_V_inv,
B_nu = self$B_nu,
hyper_nu_B = self$hyper_nu_B,
hyper_a_B = self$hyper_a_B,
hyper_s_BB = self$hyper_s_BB,
hyper_nu_BB = self$hyper_nu_BB,
hyper_nu_A = self$hyper_nu_A,
hyper_a_A = self$hyper_a_A,
hyper_s_AA = self$hyper_s_AA,
hyper_nu_AA = self$hyper_nu_AA
)
} # END get_prior
) # END public
) # END specify_prior_bsvar
#' R6 Class Representing StartingValuesBSVAR
#'
#' @description
#' The class StartingValuesBSVAR presents starting values for the homoskedastic bsvar model.
#'
#' @examples
#' # starting values for a homoskedastic bsvar for a 3-variable system
#' sv = specify_starting_values_bsvar$new(N = 3, p = 1)
#'
#' @export
specify_starting_values_bsvar = R6::R6Class(
"StartingValuesBSVAR",
public = list(
#' @field A an \code{NxK} matrix of starting values for the parameter \eqn{A}.
A = matrix(),
#' @field B an \code{NxN} matrix of starting values for the parameter \eqn{B}.
B = matrix(),
#' @field hyper a \code{(2*N+1)x2} matrix of starting values for the shrinkage hyper-parameters of the
#' hierarchical prior distribution.
hyper = matrix(),
#' @description
#' Create new starting values StartingValuesBSVAR.
#' @param N a positive integer - the number of dependent variables in the model.
#' @param p a positive integer - the autoregressive lag order of the SVAR model.
#' @param d a positive integer - the number of \code{exogenous} variables in the model.
#' @return Starting values StartingValuesBSVAR.
#' @examples
#' # starting values for a homoskedastic bsvar with 4 lags for a 3-variable system
#' sv = specify_starting_values_bsvar$new(N = 3, p = 4)
#'
initialize = function(N, p, d = 0){
stopifnot("Argument N must be a positive integer number." = N > 0 & N %% 1 == 0)
stopifnot("Argument p must be a positive integer number." = p > 0 & p %% 1 == 0)
stopifnot("Argument d must be a non-negative integer number." = d >= 0 & d %% 1 == 0)
K = N * p + 1 + d
self$B = diag(N)
self$A = cbind(diag(runif(N)), matrix(0, N, K - N))
self$hyper = matrix(10, 2 * N + 1, 2)
}, # END initialize
#' @description
#' Returns the elements of the starting values StartingValuesBSVAR as a \code{list}.
#'
#' @examples
#' # starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
#' sv = specify_starting_values_bsvar$new(N = 3, p = 1)
#' sv$get_starting_values() # show starting values as list
#'
get_starting_values = function(){
list(
B = self$B,
A = self$A,
hyper = self$hyper
)
}, # END get_starting_values
#' @description
#' Returns the elements of the starting values StartingValuesBSVAR as a \code{list}.
#' @param last_draw a list containing the last draw of elements \code{B} - an \code{NxN} matrix,
#' \code{A} - an \code{NxK} matrix, and \code{hyper} - a vector of 5 positive real numbers.
#' @return An object of class StartingValuesBSVAR including the last draw of the current MCMC
#' as the starting value to be passed to the continuation of the MCMC estimation using \code{estimate()}.
#'
#' @examples
#' # starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
#' sv = specify_starting_values_bsvar$new(N = 3, p = 1)
#'
#' # Modify the starting values by:
#' sv_list = sv$get_starting_values() # getting them as list
#' sv_list$A <- matrix(rnorm(12), 3, 4) # modifying the entry
#' sv$set_starting_values(sv_list) # providing to the class object
#'
set_starting_values = function(last_draw) {
self$B = last_draw$B
self$A = last_draw$A
self$hyper = last_draw$hyper
} # END set_starting_values
) # END public
) # END specify_starting_values_bsvar
#' R6 Class Representing IdentificationBSVARs
#'
#' @description
#' The class IdentificationBSVARs presents the identifying restrictions for the bsvar models.
#'
#' @examples
#' specify_identification_bsvars$new(N = 3) # recursive specification for a 3-variable system
#'
#' B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#' specify_identification_bsvars$new(N = 3, B = B) # an alternative identification pattern
#'
#' @export
specify_identification_bsvars = R6::R6Class(
"IdentificationBSVARs",
public = list(
#' @field VB a list of \code{N} matrices determining the unrestricted elements of matrix \eqn{B}.
VB = list(),
#' @description
#' Create new identifying restrictions IdentificationBSVARs.
#' @param N a positive integer - the number of dependent variables in the model.
#' @param B a logical \code{NxN} matrix containing value \code{TRUE} for the elements of
#' the structural matrix \eqn{B} to be estimated and value \code{FALSE} for exclusion restrictions
#' to be set to zero.
#' @return Identifying restrictions IdentificationBSVARs.
initialize = function(N, B) {
if (missing(B)) {
B = matrix(FALSE, N, N)
B[lower.tri(B, diag = TRUE)] = TRUE
}
stopifnot("Argument B must be an NxN matrix with logical values." = is.logical(B) & is.matrix(B) & prod(dim(B) == N))
self$VB <- vector("list", N)
for (n in 1:N) {
self$VB[[n]] <- matrix(diag(N)[B[n,],], ncol = N)
}
}, # END initialize
#' @description
#' Returns the elements of the identification pattern IdentificationBSVARs as a \code{list}.
#'
#' @examples
#' B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#' spec = specify_identification_bsvars$new(N = 3, B = B)
#' spec$get_identification()
#'
get_identification = function() {
as.list(self$VB)
}, # END get_identification
#' @description
#' Set new starting values StartingValuesBSVAR.
#' @param N a positive integer - the number of dependent variables in the model.
#' @param B a logical \code{NxN} matrix containing value \code{TRUE} for the elements of
#' the structural matrix \eqn{B} to be estimated and value \code{FALSE} for exclusion restrictions
#' to be set to zero.
#'
#' @examples
#' spec = specify_identification_bsvars$new(N = 3) # specify a model with the default option
#' B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#' spec$set_identification(N = 3, B = B) # modify an existing specification
#' spec$get_identification() # check the outcome
set_identification = function(N, B) {
if (missing(B)) {
B = matrix(FALSE, N, N)
B[lower.tri(B, diag = TRUE)] = TRUE
}
stopifnot("Argument B must be an NxN matrix with logical values." = is.logical(B) & is.matrix(B) & prod(dim(B) == N))
self$VB <- vector("list", N)
for (n in 1:N) {
self$VB[[n]] <- matrix(diag(N)[B[n,],], ncol = N)
}
} # END set_identification
) # END public
) # END specify_identification_bsvars
#' R6 Class Representing DataMatricesBSVAR
#'
#' @description
#' The class DataMatricesBSVAR presents the data matrices of dependent variables, \eqn{Y},
#' and regressors, \eqn{X}, for the homoskedastic bsvar model.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' YX = specify_data_matrices$new(data = us_fiscal_lsuw, p = 4)
#' dim(YX$Y); dim(YX$X)
#'
#' @export
specify_data_matrices = R6::R6Class(
"DataMatricesBSVAR",
public = list(
#' @field Y an \code{NxT} matrix of dependent variables, \eqn{Y}.
Y = matrix(),
#' @field X an \code{KxT} matrix of regressors, \eqn{X}.
X = matrix(),
#' @description
#' Create new data matrices DataMatricesBSVAR.
#' @param data a \code{(T+p)xN} matrix with time series data.
#' @param p a positive integer providing model's autoregressive lag order.
#' @param exogenous a \code{(T+p)xd} matrix of exogenous variables.
#' This matrix should not include a constant term.
#' @return New data matrices DataMatricesBSVAR.
initialize = function(data, p = 1L, exogenous = NULL) {
if (missing(data)) {
stop("Argument data has to be specified")
} else {
stopifnot("Argument data has to be a matrix." = is.matrix(data) & is.numeric(data))
stopifnot("Argument data has to contain at least 2 columns and 3 rows." = (ncol(data) >= 2 & nrow(data) >= 3))
stopifnot("Argument data cannot include missing values." = sum(is.na(data)) == 0 )
}
stopifnot("Argument p must be a positive integer number." = p > 0 & p %% 1 == 0)
if (is.null(exogenous)) {
d = 0
} else {
stopifnot("Argument exogenous has to be a matrix." = is.matrix(exogenous) & is.numeric(exogenous))
stopifnot("Argument exogenous has to contain at the same number of rows as argument data." = (ncol(exogenous) >= 1 & nrow(data) == nrow(exogenous)))
stopifnot("Argument exogenous cannot include missing values." = sum(is.na(exogenous)) == 0 )
d = ncol(exogenous)
Td = nrow(exogenous)
test_exogenous = 0
for (i in 1:d) {
test_exogenous = test_exogenous + prod(exogenous[,i] - mean(exogenous[,i]) == rep(0,Td))
}
stopifnot("Argument exogenous cannot include a constant term." = test_exogenous == 0 )
}
TT = nrow(data)
T = TT - p
Y = t(data[(p + 1):TT,])
colnames(Y) = 1:T
rownames(Y) = paste0("v", 1:ncol(data))
if ( any(class(data) == "ts") ) {
colnames(Y) = as.numeric(time(data))[(p + 1):TT]
}
if ( !is.null(colnames(data)) ) {
rownames(Y) = colnames(data)
}
self$Y = Y
X = matrix(0, T, 0)
for (i in 1:p) {
X = cbind(X, data[(p + 1):TT - i,])
}
X = cbind(X, rep(1, T))
if (!is.null(exogenous)) {
X = cbind(X, exogenous[(p + 1):TT,])
}
self$X = t(X)
}, # END initialize
#' @description
#' Returns the data matrices DataMatricesBSVAR as a \code{list}.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' YX = specify_data_matrices$new(data = us_fiscal_lsuw, p = 4)
#' YX$get_data_matrices()
#'
get_data_matrices = function() {
list(
Y = self$Y,
X = self$X
)
} # END get_data_matrices
) # END public
) # END specify_data_matrices
#' R6 Class representing the specification of the homoskedastic BSVAR model
#'
#' @description
#' The class BSVAR presents complete specification for the homoskedastic bsvar model.
#'
#' @seealso \code{\link{estimate}}, \code{\link{specify_posterior_bsvar}}
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#'
#' @export
specify_bsvar = R6::R6Class(
"BSVAR",
public = list(
#' @field p a non-negative integer specifying the autoregressive lag order of the model.
p = numeric(),
#' @field identification an object IdentificationBSVAR with the identifying restrictions.
identification = list(),
#' @field prior an object PriorBSVAR with the prior specification.
prior = list(),
#' @field data_matrices an object DataMatricesBSVAR with the data matrices.
data_matrices = list(),
#' @field starting_values an object StartingValuesBSVAR with the starting values.
starting_values = list(),
#' @description
#' Create a new specification of the homoskedastic bsvar model BSVAR.
#' @param data a \code{(T+p)xN} matrix with time series data.
#' @param p a positive integer providing model's autoregressive lag order.
#' @param B a logical \code{NxN} matrix containing value \code{TRUE} for the elements of
#' the structural matrix \eqn{B} to be estimated and value \code{FALSE} for exclusion restrictions
#' to be set to zero.
#' @param exogenous a \code{(T+p)xd} matrix of exogenous variables.
#' @param stationary an \code{N} logical vector - its element set to \code{FALSE} sets
#' the prior mean for the autoregressive parameters of the \code{N}th equation to the white noise process,
#' otherwise to random walk.
#' @return A new complete specification for the homoskedastic bsvar model BSVAR.
initialize = function(
data,
p = 1L,
B,
exogenous = NULL,
stationary = rep(FALSE, ncol(data))
) {
stopifnot("Argument p has to be a positive integer." = ((p %% 1) == 0 & p > 0))
self$p = p
TT = nrow(data)
T = TT - self$p
N = ncol(data)
d = 0
if (!is.null(exogenous)) {
d = ncol(exogenous)
}
if (missing(B)) {
message("The identification is set to the default option of lower-triangular structural matrix.")
B = matrix(FALSE, N, N)
B[lower.tri(B, diag = TRUE)] = TRUE
}
stopifnot("Incorrectly specified argument B." = (is.matrix(B) & is.logical(B)) | (length(B) == 1 & is.na(B)))
self$data_matrices = specify_data_matrices$new(data, p, exogenous)
self$identification = specify_identification_bsvars$new(N, B)
self$prior = specify_prior_bsvar$new(N, p, d, stationary)
self$starting_values = specify_starting_values_bsvar$new(N, self$p, d)
}, # END initialize
#' @description
#' Returns the data matrices as the DataMatricesBSVAR object.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#' spec$get_data_matrices()
#'
get_data_matrices = function() {
self$data_matrices$clone()
}, # END get_data_matrices
#' @description
#' Returns the identifying restrictions as the IdentificationBSVARs object.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#' spec$get_identification()
#'
get_identification = function() {
self$identification$clone()
}, # END get_identification
#' @description
#' Returns the prior specification as the PriorBSVAR object.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#' spec$get_prior()
#'
get_prior = function() {
self$prior$clone()
}, # END get_prior
#' @description
#' Returns the starting values as the StartingValuesBSVAR object.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#' spec$get_starting_values()
#'
get_starting_values = function() {
self$starting_values$clone()
} # END get_starting_values
) # END public
) # END specify_bsvar
#' R6 Class Representing PosteriorBSVAR
#'
#' @description
#' The class PosteriorBSVAR contains posterior output and the specification including
#' the last MCMC draw for the homoskedastic bsvar model.
#' Note that due to the thinning of the MCMC output the starting value in element \code{last_draw}
#' might not be equal to the last draw provided in element \code{posterior}.
#'
#' @seealso \code{\link{estimate}}, \code{\link{specify_bsvar}}
#'
#' @examples
#' # This is a function that is used within estimate()
#' data(us_fiscal_lsuw)
#' specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#' estimate = estimate(specification, 50)
#' class(estimate)
#'
#' @export
specify_posterior_bsvar = R6::R6Class(
"PosteriorBSVAR",
private = list(
normalised = FALSE
), # END private
public = list(
#' @field 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()}.
last_draw = list(),
#' @field posterior a list containing Bayesian estimation output collected in elements
#' an \code{NxNxS} array \code{B}, an \code{NxKxS} array \code{A}, and a \code{5xS} matrix \code{hyper}.
posterior = list(),
#' @description
#' Create a new posterior output PosteriorBSVAR.
#' @param specification_bsvar an object of class BSVAR with the last draw of the current
#' MCMC run as the starting value.
#' @param posterior_bsvar a list containing Bayesian estimation output collected in elements
#' an \code{NxNxS} array \code{B}, an \code{NxKxS} array \code{A}, and a \code{5xS} matrix \code{hyper}.
#' @return A posterior output PosteriorBSVAR.
initialize = function(specification_bsvar, posterior_bsvar) {
stopifnot("Argument specification_bsvar must be of class BSVAR." = any(class(specification_bsvar) == "BSVAR"))
stopifnot("Argument posterior_bsvar must must contain MCMC output." = is.list(posterior_bsvar) & is.array(posterior_bsvar$B) & is.array(posterior_bsvar$A) & is.array(posterior_bsvar$hyper))
self$last_draw = specification_bsvar
self$posterior = posterior_bsvar
}, # END initialize
#' @description
#' Returns a list containing Bayesian estimation output collected in elements
#' an \code{NxNxS} array \code{B}, an \code{NxKxS} array \code{A}, and a \code{5xS} matrix \code{hyper}.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' specification = specify_bsvar$new(us_fiscal_lsuw)
#' set.seed(123)
#' estimate = estimate(specification, 50)
#' estimate$get_posterior()
#'
get_posterior = function(){
self$posterior
}, # END get_posterior
#' @description
#' Returns 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()}.
#'
#' @examples
#' data(us_fiscal_lsuw)
#'
#' # specify the model and set seed
#' specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#'
#' # run the burn-in
#' burn_in = estimate(specification, 10)
#'
#' # estimate the model
#' posterior = estimate(burn_in, 10)
#'
get_last_draw = function(){
self$last_draw$clone()
}, # END get_last_draw
#' @description
#' Returns \code{TRUE} if the posterior has been normalised using \code{normalise_posterior()}
#' and \code{FALSE} otherwise.
#'
#' @examples
#' # upload data
#' data(us_fiscal_lsuw)
#'
#' # specify the model and set seed
#' specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#'
#' # estimate the model
#' posterior = estimate(specification, 10, thin = 1)
#'
#' # check normalisation status beforehand
#' posterior$is_normalised()
#'
#' # normalise the posterior
#' BB = posterior$last_draw$starting_values$B # get the last draw of B
#' B_hat = diag((-1) * sign(diag(BB))) %*% BB # set negative diagonal elements
#' normalise_posterior(posterior, B_hat) # draws in posterior are normalised
#'
#' # check normalisation status afterwards
#' posterior$is_normalised()
#'
is_normalised = function(){
private$normalised
}, # END is_normalised
#' @description
#' Sets the private indicator \code{normalised} to TRUE.
#' @param value (optional) a logical value to be passed to indicator \code{normalised}.
#'
#' @examples
#' # This is an internal function that is run while executing normalise_posterior()
#' # Observe its working by analysing the workflow:
#'
#' # upload data
#' data(us_fiscal_lsuw)
#'
#' # specify the model and set seed
#' specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#'
#' # estimate the model
#' posterior = estimate(specification, 10, thin = 1)
#'
#' # check normalisation status beforehand
#' posterior$is_normalised()
#'
#' # normalise the posterior
#' BB = posterior$last_draw$starting_values$B # get the last draw of B
#' B_hat = diag(sign(diag(BB))) %*% BB # set positive diagonal elements
#' normalise_posterior(posterior, B_hat) # draws in posterior are normalised
#'
#' # check normalisation status afterwards
#' posterior$is_normalised()
#'
set_normalised = function(value){
if (missing(value)) {
private$normalised <- TRUE
} else {
private$normalised <- value
}
} # END set_normalised
) # END public
) # END specify_posterior_bsvar
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#' R6 Class Representing PriorBSVAR
#'
#' @description
#' The class PriorBSVAR presents a prior specification for the homoskedastic bsvar model.
#'
#' @examples
#' prior = specify_prior_bsvar$new(N = 3, p = 1) # a prior for 3-variable example with one lag
#' prior$A # show autoregressive prior mean
#'
#' @export
specify_prior_bsvar = R6::R6Class(
"PriorBSVAR",
public = list(
#' @field A an \code{NxK} matrix, the mean of the normal prior distribution for the parameter matrix \eqn{A}.
A = matrix(),
#' @field A_V_inv a \code{KxK} precision matrix of the normal prior distribution for each of
#' the row of the parameter matrix \eqn{A}. This precision matrix is equation invariant.
A_V_inv = matrix(),
#' @field B_V_inv an \code{NxN} precision matrix of the generalised-normal prior distribution
#' for the structural matrix \eqn{B}. This precision matrix is equation invariant.
B_V_inv = matrix(),
#' @field B_nu a positive integer greater of equal than \code{N}, a shape parameter of
#' the generalised-normal prior distribution for the structural matrix \eqn{B}.
B_nu = NA,
#' @field hyper_nu_B a positive scalar, the shape parameter of the inverted-gamma 2 prior
#' for the overall shrinkage parameter for matrix \eqn{B}.
hyper_nu_B = NA,
#' @field hyper_a_B a positive scalar, the shape parameter of the gamma prior
#' for the second-level hierarchy for the overall shrinkage parameter for matrix \eqn{B}.
hyper_a_B = NA,
#' @field hyper_s_BB a positive scalar, the scale parameter of the inverted-gamma 2 prior
#' for the third-level of hierarchy for overall shrinkage parameter for matrix \eqn{B}.
hyper_s_BB = NA,
#' @field hyper_nu_BB a positive scalar, the shape parameter of the inverted-gamma 2 prior
#' for the third-level of hierarchy for overall shrinkage parameter for matrix \eqn{B}.
hyper_nu_BB = NA,
#' @field hyper_nu_A a positive scalar, the shape parameter of the inverted-gamma 2 prior
#' for the overall shrinkage parameter for matrix \eqn{A}.
hyper_nu_A = NA,
#' @field hyper_a_A a positive scalar, the shape parameter of the gamma prior
#' for the second-level hierarchy for the overall shrinkage parameter for matrix \eqn{A}.
hyper_a_A = NA,
#' @field hyper_s_AA a positive scalar, the scale parameter of the inverted-gamma 2 prior
#' for the third-level of hierarchy for overall shrinkage parameter for matrix \eqn{A}.
hyper_s_AA = NA,
#' @field hyper_nu_AA a positive scalar, the shape parameter of the inverted-gamma 2 prior
#' for the third-level of hierarchy for overall shrinkage parameter for matrix \eqn{A}.
hyper_nu_AA = NA,
#' @description
#' Create a new prior specification PriorBSVAR.
#' @param N a positive integer - the number of dependent variables in the model.
#' @param p a positive integer - the autoregressive lag order of the SVAR model.
#' @param d a positive integer - the number of \code{exogenous} variables in the model.
#' @param stationary an \code{N} logical vector - its element set to \code{FALSE} sets
#' the prior mean for the autoregressive parameters of the \code{N}th equation to the white noise process,
#' otherwise to random walk.
#' @return A new prior specification PriorBSVAR.
#' @examples
#' # a prior for 3-variable example with one lag and stationary data
#' prior = specify_prior_bsvar$new(N = 3, p = 1, stationary = rep(TRUE, 3))
#' prior$A # show autoregressive prior mean
#'
initialize = function(N, p, d = 0, stationary = rep(FALSE, N)){
stopifnot("Argument N must be a positive integer number." = N > 0 & N %% 1 == 0)
stopifnot("Argument p must be a positive integer number." = p > 0 & p %% 1 == 0)
stopifnot("Argument d must be a non-negative integer number." = d >= 0 & d %% 1 == 0)
stopifnot("Argument stationary must be a logical vector of length N." = length(stationary) == N & is.logical(stationary))
K = N * p + 1 + d
self$A = cbind(diag(as.numeric(!stationary)), matrix(0, N, K - N))
self$A_V_inv = diag(c(kronecker((1:p)^2, rep(1, N) ), rep(1, d + 1)))
self$B_V_inv = diag(N)
self$B_nu = N
self$hyper_nu_B = 10
self$hyper_a_B = 10
self$hyper_s_BB = 100
self$hyper_nu_BB = 1
self$hyper_nu_A = 10
self$hyper_a_A = 10
self$hyper_s_AA = 10
self$hyper_nu_AA = 10
}, # END initialize
#' @description
#' Returns the elements of the prior specification PriorBSVAR as a \code{list}.
#'
#' @examples
#' # a prior for 3-variable example with four lags
#' prior = specify_prior_bsvar$new(N = 3, p = 4)
#' prior$get_prior() # show the prior as list
#'
get_prior = function(){
list(
A = self$A,
A_V_inv = self$A_V_inv,
B_V_inv = self$B_V_inv,
B_nu = self$B_nu,
hyper_nu_B = self$hyper_nu_B,
hyper_a_B = self$hyper_a_B,
hyper_s_BB = self$hyper_s_BB,
hyper_nu_BB = self$hyper_nu_BB,
hyper_nu_A = self$hyper_nu_A,
hyper_a_A = self$hyper_a_A,
hyper_s_AA = self$hyper_s_AA,
hyper_nu_AA = self$hyper_nu_AA
)
} # END get_prior
) # END public
) # END specify_prior_bsvar
#' R6 Class Representing StartingValuesBSVAR
#'
#' @description
#' The class StartingValuesBSVAR presents starting values for the homoskedastic bsvar model.
#'
#' @examples
#' # starting values for a homoskedastic bsvar for a 3-variable system
#' sv = specify_starting_values_bsvar$new(N = 3, p = 1)
#'
#' @export
specify_starting_values_bsvar = R6::R6Class(
"StartingValuesBSVAR",
public = list(
#' @field A an \code{NxK} matrix of starting values for the parameter \eqn{A}.
A = matrix(),
#' @field B an \code{NxN} matrix of starting values for the parameter \eqn{B}.
B = matrix(),
#' @field hyper a \code{(2*N+1)x2} matrix of starting values for the shrinkage hyper-parameters of the
#' hierarchical prior distribution.
hyper = matrix(),
#' @description
#' Create new starting values StartingValuesBSVAR.
#' @param N a positive integer - the number of dependent variables in the model.
#' @param p a positive integer - the autoregressive lag order of the SVAR model.
#' @param d a positive integer - the number of \code{exogenous} variables in the model.
#' @return Starting values StartingValuesBSVAR.
#' @examples
#' # starting values for a homoskedastic bsvar with 4 lags for a 3-variable system
#' sv = specify_starting_values_bsvar$new(N = 3, p = 4)
#'
initialize = function(N, p, d = 0){
stopifnot("Argument N must be a positive integer number." = N > 0 & N %% 1 == 0)
stopifnot("Argument p must be a positive integer number." = p > 0 & p %% 1 == 0)
stopifnot("Argument d must be a non-negative integer number." = d >= 0 & d %% 1 == 0)
K = N * p + 1 + d
self$B = diag(N)
self$A = cbind(diag(runif(N)), matrix(0, N, K - N))
self$hyper = matrix(10, 2 * N + 1, 2)
}, # END initialize
#' @description
#' Returns the elements of the starting values StartingValuesBSVAR as a \code{list}.
#'
#' @examples
#' # starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
#' sv = specify_starting_values_bsvar$new(N = 3, p = 1)
#' sv$get_starting_values() # show starting values as list
#'
get_starting_values = function(){
list(
B = self$B,
A = self$A,
hyper = self$hyper
)
}, # END get_starting_values
#' @description
#' Returns the elements of the starting values StartingValuesBSVAR as a \code{list}.
#' @param last_draw a list containing the last draw of elements \code{B} - an \code{NxN} matrix,
#' \code{A} - an \code{NxK} matrix, and \code{hyper} - a vector of 5 positive real numbers.
#' @return An object of class StartingValuesBSVAR including the last draw of the current MCMC
#' as the starting value to be passed to the continuation of the MCMC estimation using \code{estimate()}.
#'
#' @examples
#' # starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
#' sv = specify_starting_values_bsvar$new(N = 3, p = 1)
#'
#' # Modify the starting values by:
#' sv_list = sv$get_starting_values() # getting them as list
#' sv_list$A <- matrix(rnorm(12), 3, 4) # modifying the entry
#' sv$set_starting_values(sv_list) # providing to the class object
#'
set_starting_values = function(last_draw) {
self$B = last_draw$B
self$A = last_draw$A
self$hyper = last_draw$hyper
} # END set_starting_values
) # END public
) # END specify_starting_values_bsvar
#' R6 Class Representing IdentificationBSVARs
#'
#' @description
#' The class IdentificationBSVARs presents the identifying restrictions for the bsvar models.
#'
#' @examples
#' specify_identification_bsvars$new(N = 3) # recursive specification for a 3-variable system
#'
#' B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#' specify_identification_bsvars$new(N = 3, B = B) # an alternative identification pattern
#'
#' @export
specify_identification_bsvars = R6::R6Class(
"IdentificationBSVARs",
public = list(
#' @field VB a list of \code{N} matrices determining the unrestricted elements of matrix \eqn{B}.
VB = list(),
#' @description
#' Create new identifying restrictions IdentificationBSVARs.
#' @param N a positive integer - the number of dependent variables in the model.
#' @param B a logical \code{NxN} matrix containing value \code{TRUE} for the elements of
#' the structural matrix \eqn{B} to be estimated and value \code{FALSE} for exclusion restrictions
#' to be set to zero.
#' @return Identifying restrictions IdentificationBSVARs.
initialize = function(N, B) {
if (missing(B)) {
B = matrix(FALSE, N, N)
B[lower.tri(B, diag = TRUE)] = TRUE
}
stopifnot("Argument B must be an NxN matrix with logical values." = is.logical(B) & is.matrix(B) & prod(dim(B) == N))
self$VB <- vector("list", N)
for (n in 1:N) {
self$VB[[n]] <- matrix(diag(N)[B[n,],], ncol = N)
}
}, # END initialize
#' @description
#' Returns the elements of the identification pattern IdentificationBSVARs as a \code{list}.
#'
#' @examples
#' B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#' spec = specify_identification_bsvars$new(N = 3, B = B)
#' spec$get_identification()
#'
get_identification = function() {
as.list(self$VB)
}, # END get_identification
#' @description
#' Set new starting values StartingValuesBSVAR.
#' @param N a positive integer - the number of dependent variables in the model.
#' @param B a logical \code{NxN} matrix containing value \code{TRUE} for the elements of
#' the structural matrix \eqn{B} to be estimated and value \code{FALSE} for exclusion restrictions
#' to be set to zero.
#'
#' @examples
#' spec = specify_identification_bsvars$new(N = 3) # specify a model with the default option
#' B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#' spec$set_identification(N = 3, B = B) # modify an existing specification
#' spec$get_identification() # check the outcome
set_identification = function(N, B) {
if (missing(B)) {
B = matrix(FALSE, N, N)
B[lower.tri(B, diag = TRUE)] = TRUE
}
stopifnot("Argument B must be an NxN matrix with logical values." = is.logical(B) & is.matrix(B) & prod(dim(B) == N))
self$VB <- vector("list", N)
for (n in 1:N) {
self$VB[[n]] <- matrix(diag(N)[B[n,],], ncol = N)
}
} # END set_identification
) # END public
) # END specify_identification_bsvars
#' R6 Class Representing DataMatricesBSVAR
#'
#' @description
#' The class DataMatricesBSVAR presents the data matrices of dependent variables, \eqn{Y},
#' and regressors, \eqn{X}, for the homoskedastic bsvar model.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' YX = specify_data_matrices$new(data = us_fiscal_lsuw, p = 4)
#' dim(YX$Y); dim(YX$X)
#'
#' @export
specify_data_matrices = R6::R6Class(
"DataMatricesBSVAR",
public = list(
#' @field Y an \code{NxT} matrix of dependent variables, \eqn{Y}.
Y = matrix(),
#' @field X an \code{KxT} matrix of regressors, \eqn{X}.
X = matrix(),
#' @description
#' Create new data matrices DataMatricesBSVAR.
#' @param data a \code{(T+p)xN} matrix with time series data.
#' @param p a positive integer providing model's autoregressive lag order.
#' @param exogenous a \code{(T+p)xd} matrix of exogenous variables.
#' This matrix should not include a constant term.
#' @return New data matrices DataMatricesBSVAR.
initialize = function(data, p = 1L, exogenous = NULL) {
if (missing(data)) {
stop("Argument data has to be specified")
} else {
stopifnot("Argument data has to be a matrix." = is.matrix(data) & is.numeric(data))
stopifnot("Argument data has to contain at least 2 columns and 3 rows." = (ncol(data) >= 2 & nrow(data) >= 3))
stopifnot("Argument data cannot include missing values." = sum(is.na(data)) == 0 )
}
stopifnot("Argument p must be a positive integer number." = p > 0 & p %% 1 == 0)
if (is.null(exogenous)) {
d = 0
} else {
stopifnot("Argument exogenous has to be a matrix." = is.matrix(exogenous) & is.numeric(exogenous))
stopifnot("Argument exogenous has to contain at the same number of rows as argument data." = (ncol(exogenous) >= 1 & nrow(data) == nrow(exogenous)))
stopifnot("Argument exogenous cannot include missing values." = sum(is.na(exogenous)) == 0 )
d = ncol(exogenous)
Td = nrow(exogenous)
test_exogenous = 0
for (i in 1:d) {
test_exogenous = test_exogenous + prod(exogenous[,i] - mean(exogenous[,i]) == rep(0,Td))
}
stopifnot("Argument exogenous cannot include a constant term." = test_exogenous == 0 )
}
TT = nrow(data)
T = TT - p
Y = t(data[(p + 1):TT,])
colnames(Y) = 1:T
rownames(Y) = paste0("v", 1:ncol(data))
if ( any(class(data) == "ts") ) {
colnames(Y) = as.numeric(time(data))[(p + 1):TT]
}
if ( !is.null(colnames(data)) ) {
rownames(Y) = colnames(data)
}
self$Y = Y
X = matrix(0, T, 0)
for (i in 1:p) {
X = cbind(X, data[(p + 1):TT - i,])
}
X = cbind(X, rep(1, T))
if (!is.null(exogenous)) {
X = cbind(X, exogenous[(p + 1):TT,])
}
self$X = t(X)
}, # END initialize
#' @description
#' Returns the data matrices DataMatricesBSVAR as a \code{list}.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' YX = specify_data_matrices$new(data = us_fiscal_lsuw, p = 4)
#' YX$get_data_matrices()
#'
get_data_matrices = function() {
list(
Y = self$Y,
X = self$X
)
} # END get_data_matrices
) # END public
) # END specify_data_matrices
#' R6 Class representing the specification of the homoskedastic BSVAR model
#'
#' @description
#' The class BSVAR presents complete specification for the homoskedastic bsvar model.
#'
#' @seealso \code{\link{estimate}}, \code{\link{specify_posterior_bsvar}}
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#'
#' @export
specify_bsvar = R6::R6Class(
"BSVAR",
public = list(
#' @field p a non-negative integer specifying the autoregressive lag order of the model.
p = numeric(),
#' @field identification an object IdentificationBSVAR with the identifying restrictions.
identification = list(),
#' @field prior an object PriorBSVAR with the prior specification.
prior = list(),
#' @field data_matrices an object DataMatricesBSVAR with the data matrices.
data_matrices = list(),
#' @field starting_values an object StartingValuesBSVAR with the starting values.
starting_values = list(),
#' @description
#' Create a new specification of the homoskedastic bsvar model BSVAR.
#' @param data a \code{(T+p)xN} matrix with time series data.
#' @param p a positive integer providing model's autoregressive lag order.
#' @param B a logical \code{NxN} matrix containing value \code{TRUE} for the elements of
#' the structural matrix \eqn{B} to be estimated and value \code{FALSE} for exclusion restrictions
#' to be set to zero.
#' @param exogenous a \code{(T+p)xd} matrix of exogenous variables.
#' @param stationary an \code{N} logical vector - its element set to \code{FALSE} sets
#' the prior mean for the autoregressive parameters of the \code{N}th equation to the white noise process,
#' otherwise to random walk.
#' @return A new complete specification for the homoskedastic bsvar model BSVAR.
initialize = function(
data,
p = 1L,
B,
exogenous = NULL,
stationary = rep(FALSE, ncol(data))
) {
stopifnot("Argument p has to be a positive integer." = ((p %% 1) == 0 & p > 0))
self$p = p
TT = nrow(data)
T = TT - self$p
N = ncol(data)
d = 0
if (!is.null(exogenous)) {
d = ncol(exogenous)
}
if (missing(B)) {
message("The identification is set to the default option of lower-triangular structural matrix.")
B = matrix(FALSE, N, N)
B[lower.tri(B, diag = TRUE)] = TRUE
}
stopifnot("Incorrectly specified argument B." = (is.matrix(B) & is.logical(B)) | (length(B) == 1 & is.na(B)))
self$data_matrices = specify_data_matrices$new(data, p, exogenous)
self$identification = specify_identification_bsvars$new(N, B)
self$prior = specify_prior_bsvar$new(N, p, d, stationary)
self$starting_values = specify_starting_values_bsvar$new(N, self$p, d)
}, # END initialize
#' @description
#' Returns the data matrices as the DataMatricesBSVAR object.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#' spec$get_data_matrices()
#'
get_data_matrices = function() {
self$data_matrices$clone()
}, # END get_data_matrices
#' @description
#' Returns the identifying restrictions as the IdentificationBSVARs object.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#' spec$get_identification()
#'
get_identification = function() {
self$identification$clone()
}, # END get_identification
#' @description
#' Returns the prior specification as the PriorBSVAR object.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#' spec$get_prior()
#'
get_prior = function() {
self$prior$clone()
}, # END get_prior
#' @description
#' Returns the starting values as the StartingValuesBSVAR object.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' spec = specify_bsvar$new(
#' data = us_fiscal_lsuw,
#' p = 4
#' )
#' spec$get_starting_values()
#'
get_starting_values = function() {
self$starting_values$clone()
} # END get_starting_values
) # END public
) # END specify_bsvar
#' R6 Class Representing PosteriorBSVAR
#'
#' @description
#' The class PosteriorBSVAR contains posterior output and the specification including
#' the last MCMC draw for the homoskedastic bsvar model.
#' Note that due to the thinning of the MCMC output the starting value in element \code{last_draw}
#' might not be equal to the last draw provided in element \code{posterior}.
#'
#' @seealso \code{\link{estimate}}, \code{\link{specify_bsvar}}
#'
#' @examples
#' # This is a function that is used within estimate()
#' data(us_fiscal_lsuw)
#' specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#' estimate = estimate(specification, 50)
#' class(estimate)
#'
#' @export
specify_posterior_bsvar = R6::R6Class(
"PosteriorBSVAR",
private = list(
normalised = FALSE
), # END private
public = list(
#' @field 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()}.
last_draw = list(),
#' @field posterior a list containing Bayesian estimation output collected in elements
#' an \code{NxNxS} array \code{B}, an \code{NxKxS} array \code{A}, and a \code{5xS} matrix \code{hyper}.
posterior = list(),
#' @description
#' Create a new posterior output PosteriorBSVAR.
#' @param specification_bsvar an object of class BSVAR with the last draw of the current
#' MCMC run as the starting value.
#' @param posterior_bsvar a list containing Bayesian estimation output collected in elements
#' an \code{NxNxS} array \code{B}, an \code{NxKxS} array \code{A}, and a \code{5xS} matrix \code{hyper}.
#' @return A posterior output PosteriorBSVAR.
initialize = function(specification_bsvar, posterior_bsvar) {
stopifnot("Argument specification_bsvar must be of class BSVAR." = any(class(specification_bsvar) == "BSVAR"))
stopifnot("Argument posterior_bsvar must must contain MCMC output." = is.list(posterior_bsvar) & is.array(posterior_bsvar$B) & is.array(posterior_bsvar$A) & is.array(posterior_bsvar$hyper))
self$last_draw = specification_bsvar
self$posterior = posterior_bsvar
}, # END initialize
#' @description
#' Returns a list containing Bayesian estimation output collected in elements
#' an \code{NxNxS} array \code{B}, an \code{NxKxS} array \code{A}, and a \code{5xS} matrix \code{hyper}.
#'
#' @examples
#' data(us_fiscal_lsuw)
#' specification = specify_bsvar$new(us_fiscal_lsuw)
#' set.seed(123)
#' estimate = estimate(specification, 50)
#' estimate$get_posterior()
#'
get_posterior = function(){
self$posterior
}, # END get_posterior
#' @description
#' Returns 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()}.
#'
#' @examples
#' data(us_fiscal_lsuw)
#'
#' # specify the model and set seed
#' specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#'
#' # run the burn-in
#' burn_in = estimate(specification, 10)
#'
#' # estimate the model
#' posterior = estimate(burn_in, 10)
#'
get_last_draw = function(){
self$last_draw$clone()
}, # END get_last_draw
#' @description
#' Returns \code{TRUE} if the posterior has been normalised using \code{normalise_posterior()}
#' and \code{FALSE} otherwise.
#'
#' @examples
#' # upload data
#' data(us_fiscal_lsuw)
#'
#' # specify the model and set seed
#' specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#'
#' # estimate the model
#' posterior = estimate(specification, 10, thin = 1)
#'
#' # check normalisation status beforehand
#' posterior$is_normalised()
#'
#' # normalise the posterior
#' BB = posterior$last_draw$starting_values$B # get the last draw of B
#' B_hat = diag((-1) * sign(diag(BB))) %*% BB # set negative diagonal elements
#' normalise_posterior(posterior, B_hat) # draws in posterior are normalised
#'
#' # check normalisation status afterwards
#' posterior$is_normalised()
#'
is_normalised = function(){
private$normalised
}, # END is_normalised
#' @description
#' Sets the private indicator \code{normalised} to TRUE.
#' @param value (optional) a logical value to be passed to indicator \code{normalised}.
#'
#' @examples
#' # This is an internal function that is run while executing normalise_posterior()
#' # Observe its working by analysing the workflow:
#'
#' # upload data
#' data(us_fiscal_lsuw)
#'
#' # specify the model and set seed
#' specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
#' set.seed(123)
#'
#' # estimate the model
#' posterior = estimate(specification, 10, thin = 1)
#'
#' # check normalisation status beforehand
#' posterior$is_normalised()
#'
#' # normalise the posterior
#' BB = posterior$last_draw$starting_values$B # get the last draw of B
#' B_hat = diag(sign(diag(BB))) %*% BB # set positive diagonal elements
#' normalise_posterior(posterior, B_hat) # draws in posterior are normalised
#'
#' # check normalisation status afterwards
#' posterior$is_normalised()
#'
set_normalised = function(value){
if (missing(value)) {
private$normalised <- TRUE
} else {
private$normalised <- value
}
} # END set_normalised
) # END public
) # END specify_posterior_bsvar
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