R6 Class Representing PosteriorBSVAR
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 last_draw
might not be equal to the last draw provided in element posterior.
# This is a function that is used within estimate() data(us_fiscal_lsuw) specification = specify_bsvar$new(us_fiscal_lsuw, p = 4) set.seed(123) estimate = estimate(specification, 50) class(estimate) ## ------------------------------------------------ ## Method `specify_posterior_bsvar$get_posterior` ## ------------------------------------------------ data(us_fiscal_lsuw) specification = specify_bsvar$new(us_fiscal_lsuw) set.seed(123) estimate = estimate(specification, 50) estimate$get_posterior() ## ------------------------------------------------ ## Method `specify_posterior_bsvar$get_last_draw` ## ------------------------------------------------ data(us_fiscal_lsuw) # specify the model and set seed specification = specify_bsvar$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) ## ------------------------------------------------ ## Method `specify_posterior_bsvar$is_normalised` ## ------------------------------------------------ # upload data data(us_fiscal_lsuw) # specify the model and set seed specification = specify_bsvar$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$is_normalised() ## ------------------------------------------------ ## Method `specify_posterior_bsvar$set_normalised` ## ------------------------------------------------ # 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()
estimate, specify_bsvar
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 estimate().
posterior: a list containing Bayesian estimation output collected in elements an NxNxS array B, an NxKxS array A, and a 5xS matrix hyper.
new()Create a new posterior output PosteriorBSVAR.
specify_posterior_bsvar$new(specification_bsvar, posterior_bsvar)
specification_bsvar: an object of class BSVAR with the last draw of the current MCMC run as the starting value.
posterior_bsvar: a list containing Bayesian estimation output collected in elements an NxNxS array B, an NxKxS array A, and a 5xS matrix hyper.
A posterior output PosteriorBSVAR.
get_posterior()Returns a list containing Bayesian estimation output collected in elements an NxNxS array B, an NxKxS array A, and a 5xS matrix hyper.
specify_posterior_bsvar$get_posterior()
data(us_fiscal_lsuw)
specification = specify_bsvar$new(us_fiscal_lsuw)
set.seed(123)
estimate = estimate(specification, 50)
estimate$get_posterior()
get_last_draw()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 estimate().
specify_posterior_bsvar$get_last_draw()
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar$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)
is_normalised()Returns TRUE if the posterior has been normalised using normalise_posterior()
and FALSE otherwise.
specify_posterior_bsvar$is_normalised()
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar$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$is_normalised()
set_normalised()Sets the private indicator normalised to TRUE.
specify_posterior_bsvar$set_normalised(value)
value: (optional) a logical value to be passed to indicator normalised.
# 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()
clone()The objects of this class are cloneable with this method.
specify_posterior_bsvar$clone(deep = FALSE)
deep: Whether to make a deep clone.