specify_prior_bsvar_t: R6 Class Representing PriorBSVART

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

R6 Class Representing PriorBSVART

Description

The class PriorBSVART presents a prior specification for the bsvar model with t-distributed structural shocks.

Super class

bsvars::PriorBSVAR -> PriorBSVART

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

• specify_prior_bsvar_t$clone()

Inherited methods

• bsvars::PriorBSVAR$get_prior()
• bsvars::PriorBSVAR$initialize()

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Method clone()

The objects of this class are cloneable with this method.

Usage

specify_prior_bsvar_t$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

prior = specify_prior_bsvar_t$new(N = 3, p = 1)  # specify the prior
prior$A                                        # show autoregressive prior mean

bsvars documentation built on Oct. 24, 2024, 5:11 p.m.