specify_prior_bsvar_msh {bsvars}R Documentation

R6 Class Representing PriorBSVAR-MSH

Description

The class PriorBSVAR-MSH presents a prior specification for the bsvar model with Markov Switching Heteroskedasticity.

Super class

bsvars::PriorBSVAR -> PriorBSVAR-MSH

Public fields

A

an NxK matrix, the mean of the normal prior distribution for the parameter matrix A.

A_V_inv

a KxK precision matrix of the normal prior distribution for each of the row of the parameter matrix A. This precision matrix is equation invariant.

B_V_inv

an NxN precision matrix of the generalised-normal prior distribution for the structural matrix B. This precision matrix is equation invariant.

B_nu

a positive integer greater of equal than N, a shape parameter of the generalised-normal prior distribution for the structural matrix B.

hyper_nu

a positive scalar, the shape parameter of the inverted-gamma 2 prior distribution for the two overall shrinkage parameters for matrices B and A.

hyper_a

a positive scalar, the shape parameter of the gamma prior for the two overall shrinkage parameters.

hyper_V

a positive scalar, the shape parameter of the inverted-gamma 2 for the level 3 hierarchy of shrinkage parameters.

hyper_S

a positive scalar, the scale parameter of the inverted-gamma 2 for the level 3 hierarchy of shrinkage parameters.

sigma_nu

a positive scalar, the shape parameter of the inverted-gamma 2 for MS state-dependent variances of the structural shocks, \sigma^2_{n.s_t}.

sigma_s

a positive scalar, the scale parameter of the inverted-gamma 2 for MS state-dependent variances of the structural shocks, \sigma^2_{n.s_t}.

PR_TR

an MxM matrix, the matrix of hyper-parameters of the row-specific Dirichlet prior distribution for transition probabilities matrix P of the Markov process s_t.

Methods

Public methods

Method new()

Create a new prior specification PriorBSVAR-MSH.

Usage
specify_prior_bsvar_msh$new(N, p, M, stationary = rep(FALSE, N))
Arguments
N

a positive integer - the number of dependent variables in the model.

p

a positive integer - the autoregressive lag order of the SVAR model.

M

an integer greater than 1 - the number of Markov process' heteroskedastic regimes.

stationary

an N logical vector - its element set to FALSE sets the prior mean for the autoregressive parameters of the Nth equation to the white noise process, otherwise to random walk.

Returns

A new prior specification PriorBSVAR-MSH.

Method get_prior()

Returns the elements of the prior specification PriorBSVAR-MSH as a list.

Usage
specify_prior_bsvar_msh$get_prior()
Examples
# a prior for 3-variable example with four lags and two regimes
prior = specify_prior_bsvar_msh$new(N = 3, p = 4, M = 2)
prior$get_prior() # show the prior as list

Method clone()

The objects of this class are cloneable with this method.

Usage
specify_prior_bsvar_msh$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

prior = specify_prior_bsvar_msh$new(N = 3, p = 1, M = 2)  # specify the prior
prior$A                                        # show autoregressive prior mean


## ------------------------------------------------
## Method `specify_prior_bsvar_msh$get_prior`
## ------------------------------------------------

# a prior for 3-variable example with four lags and two regimes
prior = specify_prior_bsvar_msh$new(N = 3, p = 4, M = 2)
prior$get_prior() # show the prior as list
[Package bsvars version 1.0.0 Index]