specify_posterior_bsvar_t | R Documentation |
The class PosteriorBSVART contains posterior output and the specification including
the last MCMC draw for the bsvar model with t-distributed structural shocks.
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
.
last_draw
an object of class BSVART 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.
new()
Create a new posterior output PosteriorBSVART.
specify_posterior_bsvar_t$new(specification_bsvar, posterior_bsvar)
specification_bsvar
an object of class BSVART with the last draw of the current MCMC run as the starting value.
posterior_bsvar
a list containing Bayesian estimation output.
A posterior output PosteriorBSVART.
get_posterior()
Returns a list containing Bayesian estimation output.
specify_posterior_bsvar_t$get_posterior()
data(us_fiscal_lsuw) specification = specify_bsvar_t$new(us_fiscal_lsuw) set.seed(123) estimate = estimate(specification, 10) estimate$get_posterior()
get_last_draw()
Returns an object of class BSVART 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_t$get_last_draw()
data(us_fiscal_lsuw) # specify the model and set seed specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4) # run the burn-in set.seed(123) 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_t$is_normalised()
# upload data data(us_fiscal_lsuw) # specify the model and set seed specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4) # estimate the model set.seed(123) posterior = estimate(specification, 10) # 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$is_normalised()
set_normalised()
Sets the private indicator normalised
to TRUE.
specify_posterior_bsvar_t$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_t$new(us_fiscal_lsuw, p = 4) set.seed(123) # estimate the model posterior = estimate(specification, 10) # check normalisation status beforehand posterior$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_t$clone(deep = FALSE)
deep
Whether to make a deep clone.
estimate
, specify_bsvar_t
# This is a function that is used within estimate()
data(us_fiscal_lsuw)
specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4)
set.seed(123)
estimate = estimate(specification, 10)
class(estimate)
## ------------------------------------------------
## Method `specify_posterior_bsvar_t$get_posterior`
## ------------------------------------------------
data(us_fiscal_lsuw)
specification = specify_bsvar_t$new(us_fiscal_lsuw)
set.seed(123)
estimate = estimate(specification, 10)
estimate$get_posterior()
## ------------------------------------------------
## Method `specify_posterior_bsvar_t$get_last_draw`
## ------------------------------------------------
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4)
# run the burn-in
set.seed(123)
burn_in = estimate(specification, 10)
# estimate the model
posterior = estimate(burn_in, 10)
## ------------------------------------------------
## Method `specify_posterior_bsvar_t$is_normalised`
## ------------------------------------------------
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4)
# estimate the model
set.seed(123)
posterior = estimate(specification, 10)
# check normalisation status beforehand
posterior$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_t$set_normalised`
## ------------------------------------------------
# This is an internal function that is run while executing normalise_posterior()
# Observe its working by analysing the workflow:
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 4)
set.seed(123)
# estimate the model
posterior = estimate(specification, 10)
# 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()
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