specify_prior_bsvar_msh | R Documentation |
The class PriorBSVARMSH presents a prior specification for the bsvar model with Markov Switching Heteroskedasticity.
bsvars::PriorBSVAR
-> PriorBSVARMSH
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 MS state-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 MS state-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 transition probabilities matrix P
of the Markov process s_t
.
new()
Create a new prior specification PriorBSVARMSH.
specify_prior_bsvar_msh$new(N, p, d = 0, M, stationary = rep(FALSE, N))
N
a positive integer - the number of dependent variables in the model.
p
a positive integer - the autoregressive lag order of the SVAR model.
d
a positive integer - the number of exogenous
variables in the model.
M
an integer greater than 1 - the number of Markov process' heteroskedastic regimes.
stationary
an N
logical vector - its element set to FALSE
sets the prior mean for the autoregressive parameters of the N
th equation to the white noise process, otherwise to random walk.
A new prior specification PriorBSVARMSH.
get_prior()
Returns the elements of the prior specification PriorBSVARMSH as a list
.
specify_prior_bsvar_msh$get_prior()
# a prior for 3-variable example with four lags and two regimes prior = specify_prior_bsvar_msh$new(N = 3, p = 4, M = 2) prior$get_prior() # show the prior as list
clone()
The objects of this class are cloneable with this method.
specify_prior_bsvar_msh$clone(deep = FALSE)
deep
Whether to make a deep clone.
prior = specify_prior_bsvar_msh$new(N = 3, p = 1, M = 2) # specify the prior
prior$A # show autoregressive prior mean
## ------------------------------------------------
## Method `specify_prior_bsvar_msh$get_prior`
## ------------------------------------------------
# a prior for 3-variable example with four lags and two regimes
prior = specify_prior_bsvar_msh$new(N = 3, p = 4, M = 2)
prior$get_prior() # show the prior as list
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