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R6 Class Representing PriorBSVARSV

Source: R/specify_bsvar_sv.R
specify_prior_bsvar_sv.Rd

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

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

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.

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
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0


## ------------------------------------------------
## 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
#> $A
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,]    1    0    0    0    0    0    0    0    0     0     0     0     0
#> [2,]    0    1    0    0    0    0    0    0    0     0     0     0     0
#> [3,]    0    0    1    0    0    0    0    0    0     0     0     0     0
#> 
#> $A_V_inv
#>       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#>  [1,]    1    0    0    0    0    0    0    0    0     0     0     0     0
#>  [2,]    0    1    0    0    0    0    0    0    0     0     0     0     0
#>  [3,]    0    0    1    0    0    0    0    0    0     0     0     0     0
#>  [4,]    0    0    0    4    0    0    0    0    0     0     0     0     0
#>  [5,]    0    0    0    0    4    0    0    0    0     0     0     0     0
#>  [6,]    0    0    0    0    0    4    0    0    0     0     0     0     0
#>  [7,]    0    0    0    0    0    0    9    0    0     0     0     0     0
#>  [8,]    0    0    0    0    0    0    0    9    0     0     0     0     0
#>  [9,]    0    0    0    0    0    0    0    0    9     0     0     0     0
#> [10,]    0    0    0    0    0    0    0    0    0    16     0     0     0
#> [11,]    0    0    0    0    0    0    0    0    0     0    16     0     0
#> [12,]    0    0    0    0    0    0    0    0    0     0     0    16     0
#> [13,]    0    0    0    0    0    0    0    0    0     0     0     0     1
#> 
#> $B_V_inv
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> [2,]    0    1    0
#> [3,]    0    0    1
#> 
#> $B_nu
#> [1] 3
#> 
#> $hyper_nu_B
#> [1] 10
#> 
#> $hyper_a_B
#> [1] 10
#> 
#> $hyper_s_BB
#> [1] 100
#> 
#> $hyper_nu_BB
#> [1] 1
#> 
#> $hyper_nu_A
#> [1] 10
#> 
#> $hyper_a_A
#> [1] 10
#> 
#> $hyper_s_AA
#> [1] 10
#> 
#> $hyper_nu_AA
#> [1] 10
#> 
#> $sv_a_
#> [1] 1
#> 
#> $sv_s_
#> [1] 0.1
#>