specify_prior_bsvar_mix {bsvars} R Documentation

## R6 Class Representing PriorBSVAR-MIX

### Description

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

### Super classes

bsvars::PriorBSVAR -> bsvars::PriorBSVAR-MSH -> PriorBSVAR-MIX

### 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 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.

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



[Package bsvars version 1.0.0 Index]