
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
- p
- a non-negative integer specifying the autoregressive lag order of the model. 
- identification
- an object IdentificationBSVARs with the identifying restrictions. 
- prior
- an object PriorBSVARMIX with the prior specification. 
- data_matrices
- an object DataMatricesBSVAR with the data matrices. 
- starting_values
- an object StartingValuesBSVARMIX with the starting values. 
- finiteM
- a 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
- data
- a - (T+p)xNmatrix with time series data.
- p
- a positive integer providing model's autoregressive lag order. 
- M
- an integer greater than 1 - the number of components of the mixture of normals. 
- B
- a logical - NxNmatrix containing value- TRUEfor the elements of the structural matrix \(B\) to be estimated and value- FALSEfor exclusion restrictions to be set to zero.
- A
- a logical - NxKmatrix containing value- TRUEfor the elements of the autoregressive matrix \(A\) to be estimated and value- FALSEfor exclusion restrictions to be set to zero.
- distribution
- a 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.
- exogenous
- a - (T+p)xdmatrix of exogenous variables.
- stationary
- an - Nlogical vector - its element set to- FALSEsets the prior mean for the autoregressive parameters of the- Nth equation to the white noise process, otherwise to random walk.
- finiteM
- a 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.