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R6 Class Representing PriorBSVARMIX

Source: R/specify_bsvar_mix.R
specify_prior_bsvar_mix.Rd

The class PriorBSVARMIX presents a prior specification for the bsvar model with a zero-mean mixture of normals model for structural shocks.

Super classes

bsvars::PriorBSVAR -> bsvars::PriorBSVARMSH -> PriorBSVARMIX

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_B

a positive scalar, the shape parameter of the inverted-gamma 2 prior for the overall shrinkage parameter for matrix \(B\).

hyper_a_B

a positive scalar, the shape parameter of the gamma prior for the second-level hierarchy for the overall shrinkage parameter for matrix \(B\).

hyper_s_BB

a positive scalar, the scale parameter of the inverted-gamma 2 prior for the third-level of hierarchy for overall shrinkage parameter for matrix \(B\).

hyper_nu_BB

a positive scalar, the shape parameter of the inverted-gamma 2 prior for the third-level of hierarchy for overall shrinkage parameter for matrix \(B\).

hyper_nu_A

a positive scalar, the shape parameter of the inverted-gamma 2 prior for the overall shrinkage parameter for matrix \(A\).

hyper_a_A

a positive scalar, the shape parameter of the gamma prior for the second-level hierarchy for the overall shrinkage parameter for matrix \(A\).

hyper_s_AA

a positive scalar, the scale parameter of the inverted-gamma 2 prior for the third-level of hierarchy for overall shrinkage parameter for matrix \(A\).

hyper_nu_AA

a positive scalar, the shape parameter of the inverted-gamma 2 prior for the third-level of hierarchy for overall shrinkage parameter for matrix \(A\).

sigma_nu

a positive scalar, the shape parameter of the inverted-gamma 2 for mixture component-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 mixture component-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 the state probabilities the Markov process \(s_t\). Its rows must be identical.

Methods

Public methods

Inherited methods

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_prior_bsvar_mix$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

prior = specify_prior_bsvar_mix$new(N = 3, p = 1, M = 2)  # specify the prior
prior$A                                        # show autoregressive prior mean
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0