specify_prior_bsvar_msh function

R6 Class Representing PriorBSVARMSH

The class PriorBSVARMSH presents a prior specification for the bsvar model with Markov Switching Heteroskedasticity.

Examples

prior = specify_prior_bsvar_msh$new(N = 3, p = 1, M = 2)  # specify the prior
prior$A                                        # show autoregressive prior mean

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## Method `specify_prior_bsvar_msh$get_prior`
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# a prior for 3-variable example with four lags and two regimes
prior = specify_prior_bsvar_msh$new(N = 3, p = 4, M = 2)
prior$get_prior() # show the prior as list

Super class

bsvars::PriorBSVAR -> PriorBSVARMSH

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 $A$.

• sigma_nu: a positive scalar, the shape parameter of the inverted-gamma 2 for MS state-dependent variances of the structural shocks, $\sigma^2_{n.s_t}$.

• sigma_s: a positive scalar, the scale parameter of the inverted-gamma 2 for MS state-dependent variances of the structural shocks, $\sigma^2_{n.s_t}$.

• PR_TR: an MxM matrix, the matrix of hyper-parameters of the row-specific Dirichlet prior distribution for transition probabilities matrix $P$ of the Markov process $s_t$.

Methods

Public methods

• list("specify_prior_bsvar_msh$new()")

• list("specify_prior_bsvar_msh$get_prior()")

• list("specify_prior_bsvar_msh$clone()")

Method new()

Create a new prior specification PriorBSVARMSH.

Usage

specify_prior_bsvar_msh$new(N, p, d = 0, M, 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.

• M: an integer greater than 1 - the number of Markov process' heteroskedastic regimes.

• 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 PriorBSVARMSH.

Method get_prior()

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

Usage

specify_prior_bsvar_msh$get_prior()

Examples

# a prior for 3-variable example with four lags and two regimes
prior = specify_prior_bsvar_msh$new(N = 3, p = 4, M = 2)
prior$get_prior() # show the prior as list

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_prior_bsvar_msh$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.