specify_starting_values_bsvar_mix function

R6 Class Representing StartingValuesBSVARMIX

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

Examples

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

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.

Methods

Public methods

• list("specify_starting_values_bsvar_mix$new()")

• list("specify_starting_values_bsvar_mix$clone()")

Method new()

Create new starting values StartingValuesBSVARMIX.

Usage

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

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.

• 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.