| specify_prior_bsvar_mix {bsvars} | R Documentation |
The class PriorBSVAR-MIX presents a prior specification for the bsvar model with a zero-mean mixture of normals model for structural shocks.
bsvars::PriorBSVAR -> bsvars::PriorBSVAR-MSH -> PriorBSVAR-MIX
Aan NxK matrix, the mean of the normal prior distribution for the parameter matrix A.
A_V_inva 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_invan NxN precision matrix of the generalised-normal prior distribution for the structural matrix B. This precision matrix is equation invariant.
B_nua positive integer greater of equal than N, a shape parameter of the generalised-normal prior distribution for the structural matrix B.
hyper_nua positive scalar, the shape parameter of the inverted-gamma 2 prior distribution for the two overall shrinkage parameters for matrices B and A.
hyper_aa positive scalar, the shape parameter of the gamma prior for the two overall shrinkage parameters.
hyper_Va positive scalar, the shape parameter of the inverted-gamma 2 for the level 3 hierarchy of shrinkage parameters.
hyper_Sa positive scalar, the scale parameter of the inverted-gamma 2 for the level 3 hierarchy of shrinkage parameters.
sigma_nua 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_sa 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_TRan 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.
clone()The objects of this class are cloneable with this method.
specify_prior_bsvar_mix$clone(deep = FALSE)
deepWhether to make a deep clone.
prior = specify_prior_bsvar_mix$new(N = 3, p = 1, M = 2) # specify the prior
prior$A # show autoregressive prior mean