specify_prior_bsvar_mix function

R6 Class Representing PriorBSVARMIX

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

Examples

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

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

• list("specify_prior_bsvar_mix$clone()")

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.