specify_prior_bsvar_sv function

R6 Class Representing PriorBSVARSV

The class PriorBSVARSV presents a prior specification for the bsvar model with Stochastic Volatility heteroskedasticity.

Examples

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`
## ------------------------------------------------

# 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

Super class

bsvars::PriorBSVAR -> PriorBSVARSV

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 $A$.

• 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 $\sigma^2_{\omega}$.

Methods

Public methods

• list("specify_prior_bsvar_sv$new()")

• list("specify_prior_bsvar_sv$get_prior()")

• list("specify_prior_bsvar_sv$clone()")

Method new()

Create a new prior specification PriorBSVARSV.

Usage

specify_prior_bsvar_sv$new(N, p, d = 0, stationary = rep(FALSE, N))

Arguments

• 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.

• stationary: an 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.

Returns

A new prior specification PriorBSVARSV.

Method get_prior()

Returns the elements of the prior specification PriorBSVARSV as a list.

Usage

specify_prior_bsvar_sv$get_prior()

Examples

# 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

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_prior_bsvar_sv$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.