specify_prior_bsvar_mix | R Documentation |
The class PriorBSVARMIX presents a prior specification for the bsvar model with a zero-mean mixture of normals model for structural shocks.
bsvars::PriorBSVAR
-> bsvars::PriorBSVARMSH
-> PriorBSVARMIX
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.
clone()
The objects of this class are cloneable with this method.
specify_prior_bsvar_mix$clone(deep = FALSE)
deep
Whether to make a deep clone.
prior = specify_prior_bsvar_mix$new(N = 3, p = 1, M = 2) # specify the prior
prior$A # show autoregressive prior mean
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