specify_posterior_bsvar_mix {bsvars}R Documentation

R6 Class Representing PosteriorBSVARMIX

Description

The class PosteriorBSVARMIX 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

last_draw

an object of class BSVARMIX 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.

Methods

Public methods

Method new()

Create a new posterior output PosteriorBSVARMIX.

Usage
specify_posterior_bsvar_mix$new(specification_bsvar, posterior_bsvar)
Arguments
specification_bsvar

an object of class BSVARMIX with the last draw of the current MCMC run as the starting value.

posterior_bsvar

a list containing Bayesian estimation output.

Returns

A posterior output PosteriorBSVARMIX.

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(specification, 10, thin = 1)
estimate$get_posterior()

Method get_last_draw()

Returns an object of class BSVARMIX 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().

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(specification, 10, thin = 2)

# estimate the model
posterior      = estimate(burn_in, 10, 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(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 set_normalised()

Sets the private indicator normalised to TRUE.

Usage
specify_posterior_bsvar_mix$set_normalised(value)
Arguments
value

(optional) a logical value to be passed to indicator 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_mix$new(us_fiscal_lsuw, p = 4, M = 2)
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()

Method clone()

The objects of this class are cloneable with this method.

Usage
specify_posterior_bsvar_mix$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

estimate, specify_bsvar_mix

Examples

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


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

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

# estimate the model
posterior      = estimate(burn_in, 10, 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(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_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_mix$new(us_fiscal_lsuw, p = 4, M = 2)
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()
[Package bsvars version 3.1 Index]