
R6 Class representing the specification of the BSVAR model with a zero-mean mixture of normals model for structural shocks.
Source:R/specify_bsvar_mix.R
specify_bsvar_mix.RdThe class BSVARMIX presents complete specification for the BSVAR model with a zero-mean mixture of normals model for structural shocks.
Public fields
pa non-negative integer specifying the autoregressive lag order of the model.
identificationan object IdentificationBSVARs with the identifying restrictions.
prioran object PriorBSVARMIX with the prior specification.
data_matricesan object DataMatricesBSVAR with the data matrices.
starting_valuesan object StartingValuesBSVARMIX with the starting values.
finiteMa logical value - if true a finite mixture model is estimated. Otherwise, a sparse mixture model is estimated in which
M=20and the number of visited states is estimated.
Methods
Method new()
Create a new specification of the BSVAR model with a zero-mean mixture of normals model for structural shocks, BSVARMIX.
Usage
specify_bsvar_mix$new(
data,
p = 1L,
M = 2L,
B,
A,
distribution = c("norm", "t"),
exogenous = NULL,
stationary = rep(FALSE, ncol(data)),
finiteM = TRUE
)Arguments
dataa
(T+p)xNmatrix with time series data.pa positive integer providing model's autoregressive lag order.
Man integer greater than 1 - the number of components of the mixture of normals.
Ba logical
NxNmatrix containing valueTRUEfor the elements of the structural matrix \(B\) to be estimated and valueFALSEfor exclusion restrictions to be set to zero.Aa logical
NxKmatrix containing valueTRUEfor the elements of the autoregressive matrix \(A\) to be estimated and valueFALSEfor exclusion restrictions to be set to zero.distributiona character string specifying the conditional distribution of structural shocks. Value
"norm"sets it to the normal distribution, while value"t"sets the Student-t distribution.exogenousa
(T+p)xdmatrix of exogenous variables.stationaryan
Nlogical vector - its element set toFALSEsets the prior mean for the autoregressive parameters of theNth equation to the white noise process, otherwise to random walk.finiteMa logical value - if true a finite mixture model is estimated. Otherwise, a sparse mixture model is estimated in which
M=20and the number of visited states is estimated.
Examples
data(us_fiscal_lsuw)
spec = specify_bsvar_mix$new(
data = us_fiscal_lsuw,
p = 4,
M = 2
)
#> The identification is set to the default option of lower-triangular structural matrix.