| specify_prior_bsvar_sv {bsvars} | R Documentation |
The class PriorBSVAR-SV presents a prior specification for the bsvar model with Stochastic Volatility heteroskedasticity.
bsvars::PriorBSVAR -> PriorBSVAR-SV
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.
sv_a_a positive scalar, the shape parameter of the gamma prior in the hierarchical prior for \sigma^2_{\omega}.
sv_s_a positive scalar, the scale parameter of the gamma prior in the hierarchical prior for \sigma^2_{\omega}.
new()Create a new prior specification PriorBSVAR-SV.
specify_prior_bsvar_sv$new(N, p, stationary = rep(FALSE, N))
Na positive integer - the number of dependent variables in the model.
pa positive integer - the autoregressive lag order of the SVAR model.
stationaryan N logical vector - its element set to FALSE sets the prior mean for the autoregressive parameters of the Nth equation to the white noise process, otherwise to random walk.
A new prior specification PriorBSVAR-SV.
get_prior()Returns the elements of the prior specification PriorBSVAR-SV as a list.
specify_prior_bsvar_sv$get_prior()
# a prior for 3-variable example with four lags prior = specify_prior_bsvar_sv$new(N = 3, p = 4) prior$get_prior() # show the prior as list
clone()The objects of this class are cloneable with this method.
specify_prior_bsvar_sv$clone(deep = FALSE)
deepWhether to make a deep clone.
prior = specify_prior_bsvar_sv$new(N = 3, p = 1) # a prior for 3-variable example with one lag
prior$A # show autoregressive prior mean
## ------------------------------------------------
## Method `specify_prior_bsvar_sv$get_prior`
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# a prior for 3-variable example with four lags
prior = specify_prior_bsvar_sv$new(N = 3, p = 4)
prior$get_prior() # show the prior as list