specify_starting_values_bsvar_mix | R Documentation |
The class StartingValuesBSVARMIX presents starting values for the bsvar model with a zero-mean mixture of normals model for structural shocks.
bsvars::StartingValuesBSVAR
-> bsvars::StartingValuesBSVARMSH
-> StartingValuesBSVARMIX
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.
new()
Create new starting values StartingValuesBSVARMIX.
specify_starting_values_bsvar_mix$new(N, p, M, T, d = 0, finiteM = TRUE)
N
a positive integer - the number of dependent variables in the model.
p
a positive integer - the autoregressive lag order of the SVAR model.
M
an integer greater than 1 - the number of components of the mixture of normals.
T
a positive integer - the the time series dimension of the dependent variable matrix Y
.
d
a positive integer - the number of exogenous
variables in the model.
finiteM
a logical value - if true a finite mixture model is estimated. Otherwise, a sparse mixture model is estimated in which M=20
and the number of visited states is estimated.
Starting values StartingValuesBSVARMIX.
clone()
The objects of this class are cloneable with this method.
specify_starting_values_bsvar_mix$clone(deep = FALSE)
deep
Whether to make a deep clone.
# 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)
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