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R6 Class Representing StartingValuesBSVARMIX

Source: R/specify_bsvar_mix.R
specify_starting_values_bsvar_mix.Rd

The class StartingValuesBSVARMIX presents starting values for the bsvar model with a zero-mean mixture of normals model for structural shocks.

Super classes

bsvars::StartingValuesBSVAR -> bsvars::StartingValuesBSVARMSH -> StartingValuesBSVARMIX

Public fields

A

an NxK matrix of starting values for the parameter \(A\).

B

an NxN matrix of starting values for the parameter \(B\).

hyper

a (2*N+1)x2 matrix of starting values for the shrinkage hyper-parameters of the hierarchical prior distribution.

sigma2

an NxM matrix of starting values for the MS state-specific variances of the structural shocks. Its elements sum to value M over the rows.

PR_TR

an MxM matrix of starting values for the probability matrix of the Markov process. Its rows must be identical and the elements of each row sum to 1 over the rows.

xi

an MxT matrix of starting values for the Markov process indicator. Its columns are a chosen column of an identity matrix of order M.

pi_0

an M-vector of starting values for mixture components state probabilities. Its elements sum to 1.

lambda

a NxT matrix of starting values for latent variables.

df

an Nx1 vector of positive numbers with starting values for the equation-specific degrees of freedom parameters of the Student-t conditional distribution of structural shocks.

Methods

Public methods

Inherited methods

Method new()

Create new starting values StartingValuesBSVARMIX.

Usage

specify_starting_values_bsvar_mix$new(A, B, N, p, M, T, d = 0, finiteM = TRUE)

Arguments

A

a logical NxK matrix containing value TRUE for the elements of the autoregressive matrix \(A\) to be estimated and value FALSE for exclusion restrictions to be set to zero.

B

a logical NxN matrix containing value TRUE for the elements of the staructural matrix \(B\) to be estimated and value FALSE for exclusion restrictions to be set to zero.

N

a positive integer - the number of dependent variables in the model.

p

a positive integer - the autoregressive lag order of the SVAR model.

M

an integer greater than 1 - the number of components of the mixture of normals.

T

a positive integer - the the time series dimension of the dependent variable matrix \(Y\).

d

a positive integer - the number of exogenous variables in the model.

finiteM

a logical value - if true a finite mixture model is estimated. Otherwise, a sparse mixture model is estimated in which M=20 and the number of visited states is estimated.

Returns

Starting values StartingValuesBSVARMIX.

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_starting_values_bsvar_mix$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# starting values for a bsvar model for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_mix$new(A = A, B = B, N = 3, p = 1, M = 2, T = 100)