specify_prior_bsvar {bsvars}R Documentation

R6 Class Representing PriorBSVAR

Description

The class PriorBSVAR presents a prior specification for the homoskedastic bsvar model.

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.

Methods

Public methods

Method new()

Create a new prior specification PriorBSVAR.

Usage
specify_prior_bsvar$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 PriorBSVAR.

Examples
# a prior for 3-variable example with one lag and stationary data
prior = specify_prior_bsvar$new(N = 3, p = 1, stationary = rep(TRUE, 3))
prior$A # show autoregressive prior mean

Method get_prior()

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

Usage
specify_prior_bsvar$get_prior()
Examples
# a prior for 3-variable example with four lags
prior = specify_prior_bsvar$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$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

prior = specify_prior_bsvar$new(N = 3, p = 1)  # a prior for 3-variable example with one lag
prior$A                                        # show autoregressive prior mean


## ------------------------------------------------
## Method `specify_prior_bsvar$new`
## ------------------------------------------------

# a prior for 3-variable example with one lag and stationary data
prior = specify_prior_bsvar$new(N = 3, p = 1, stationary = rep(TRUE, 3))
prior$A # show autoregressive prior mean


## ------------------------------------------------
## Method `specify_prior_bsvar$get_prior`
## ------------------------------------------------

# a prior for 3-variable example with four lags
prior = specify_prior_bsvar$new(N = 3, p = 4)
prior$get_prior() # show the prior as list
[Package bsvars version 3.1 Index]