R6 Class Representing PosteriorBSVAR-MIX

Description

The class PosteriorBSVAR-MIX contains posterior output and the specification including the last MCMC draw for the bsvar model with a zero-mean mixture of normals model for 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.

Public fields

Methods

Public methods

• specify_posterior_bsvar_mix$new()

• specify_posterior_bsvar_mix$get_posterior()

• specify_posterior_bsvar_mix$get_last_draw()

• specify_posterior_bsvar_mix$is_normalised()

• specify_posterior_bsvar_mix$set_normalised()

• specify_posterior_bsvar_mix$clone()

Method new()

Create a new posterior output PosteriorBSVAR-MIX.

Usage

specify_posterior_bsvar_mix$new(specification_bsvar, posterior_bsvar)

Arguments

Returns

A posterior output PosteriorBSVAR-MIX.

Method get_posterior()

Returns a list containing Bayesian estimation output.

Usage

specify_posterior_bsvar_mix$get_posterior()

Examples

data(us_fiscal_lsuw)
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, M = 2)
set.seed(123)
estimate       = estimate_bsvar_mix(10, specification, thin = 1)
estimate$get_posterior()

Method get_last_draw()

Returns an object of class BSVAR-MIX with the last draw of the current MCMC run as the starting value to be passed to the continuation of the MCMC estimation using bsvar_mix().

Usage

specify_posterior_bsvar_mix$get_last_draw()

Examples

data(us_fiscal_lsuw)

# specify the model and set seed
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, p = 4, M = 2)

# run the burn-in
set.seed(123)
burn_in        = estimate_bsvar_mix(10, specification, thin = 2)

# get the last draw
last_draw      = burn_in$get_last_draw()

# estimate the model
posterior      = estimate_bsvar_mix(10, last_draw, thin = 2)

Method is_normalised()

Returns TRUE if the posterior has been normalised using normalise_posterior() and FALSE otherwise.

Usage

specify_posterior_bsvar_mix$is_normalised()

Examples

# upload data
data(us_fiscal_lsuw)

# specify the model and set seed
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, p = 4, M = 2)

# estimate the model
set.seed(123)
posterior      = estimate_bsvar_mix(10, specification, 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
bsvars::normalise_posterior(posterior, B_hat)              # draws in posterior are normalised

# check normalisation status afterwards
posterior$is_normalised()

Method set_normalised()

Sets the private indicator normalised to TRUE.

Usage

specify_posterior_bsvar_mix$set_normalised(value)

Arguments

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_bsvar(10, specification, 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
bsvars::normalise_posterior(posterior, B_hat)              # draws in posterior are normalised

# check normalisation status afterwards
posterior$is_normalised()

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_posterior_bsvar_mix$clone(deep = FALSE)

Arguments

See Also

estimate_bsvar_mix, specify_bsvar_mix

Examples

# This is a function that is used within estimate_bsvar()
data(us_fiscal_lsuw)
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, p = 4, M = 2)
set.seed(123)
estimate       = estimate_bsvar_mix(10, specification, thin = 1)
class(estimate)


## ------------------------------------------------
## Method `specify_posterior_bsvar_mix$get_posterior`
## ------------------------------------------------

data(us_fiscal_lsuw)
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, M = 2)
set.seed(123)
estimate       = estimate_bsvar_mix(10, specification, thin = 1)
estimate$get_posterior()


## ------------------------------------------------
## Method `specify_posterior_bsvar_mix$get_last_draw`
## ------------------------------------------------

data(us_fiscal_lsuw)

# specify the model and set seed
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, p = 4, M = 2)

# run the burn-in
set.seed(123)
burn_in        = estimate_bsvar_mix(10, specification, thin = 2)

# get the last draw
last_draw      = burn_in$get_last_draw()

# estimate the model
posterior      = estimate_bsvar_mix(10, last_draw, thin = 2)


## ------------------------------------------------
## Method `specify_posterior_bsvar_mix$is_normalised`
## ------------------------------------------------

# upload data
data(us_fiscal_lsuw)

# specify the model and set seed
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, p = 4, M = 2)

# estimate the model
set.seed(123)
posterior      = estimate_bsvar_mix(10, specification, 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
bsvars::normalise_posterior(posterior, B_hat)              # draws in posterior are normalised

# check normalisation status afterwards
posterior$is_normalised()


## ------------------------------------------------
## Method `specify_posterior_bsvar_mix$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_bsvar(10, specification, 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
bsvars::normalise_posterior(posterior, B_hat)              # draws in posterior are normalised

# check normalisation status afterwards
posterior$is_normalised()