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R6 Class Representing IdentificationBSVARs

Source: R/specify_bsvar.R
specify_identification_bsvars.Rd

The class IdentificationBSVARs presents the identifying restrictions for the bsvar models.

Public fields

VB

a list of N matrices determining the unrestricted elements of matrix \(B\).

VA

a list of N matrices determining the unrestricted elements of matrix \(A\).

Methods

Public methods

Method new()

Create new identifying restrictions IdentificationBSVARs.

Usage

specify_identification_bsvars$new(B, A, N, K)

Arguments

B

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

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.

N

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

K

a positive integer - the number of parameters in a row of autoregressive matrix.

Returns

Identifying restrictions IdentificationBSVARs.

Method get_identification()

Returns the elements of the identification pattern IdentificationBSVARs as a list.

Usage

specify_identification_bsvars$get_identification()

Examples

B    = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
spec = specify_identification_bsvars$new(B = B, N = 3, K = 4)
spec$get_identification()

Method set_identification()

Set new starting values StartingValuesBSVAR.

Usage

specify_identification_bsvars$set_identification(B, A, N, K)

Arguments

B

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

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.

N

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

K

a positive integer - the number of parameters in a row of autoregressive matrix.

Examples

spec = specify_identification_bsvars$new(N = 3, K = 4) # specify a model with the default option
B    = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
spec$set_identification(B = B, N = 3, K = 4)  # modify an existing specification
spec$get_identification()              # check the outcome

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_identification_bsvars$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

specify_identification_bsvars$new(N = 3, K = 4) # recursive specification for a 3-variable system
#> <IdentificationBSVARs>
#>   Public:
#>     VA: list
#>     VB: list
#>     clone: function (deep = FALSE) 
#>     get_identification: function () 
#>     initialize: function (B, A, N, K) 
#>     set_identification: function (B, A, N, K) 

B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#>      [,1]  [,2]  [,3]
#> [1,] TRUE FALSE FALSE
#> [2,] TRUE FALSE  TRUE
#> [3,] TRUE  TRUE  TRUE
specify_identification_bsvars$new(B = B, N = 3, K = 4) # an alternative identification pattern
#> <IdentificationBSVARs>
#>   Public:
#>     VA: list
#>     VB: list
#>     clone: function (deep = FALSE) 
#>     get_identification: function () 
#>     initialize: function (B, A, N, K) 
#>     set_identification: function (B, A, N, K) 


## ------------------------------------------------
## Method `specify_identification_bsvars$get_identification`
## ------------------------------------------------

B    = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#>      [,1]  [,2]  [,3]
#> [1,] TRUE FALSE FALSE
#> [2,] TRUE FALSE  TRUE
#> [3,] TRUE  TRUE  TRUE
spec = specify_identification_bsvars$new(B = B, N = 3, K = 4)
spec$get_identification()
#> $VB
#> $VB[[1]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> 
#> $VB[[2]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> [2,]    0    0    1
#> 
#> $VB[[3]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> [2,]    0    1    0
#> [3,]    0    0    1
#> 
#> 
#> $VA
#> $VA[[1]]
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0
#> [4,]    0    0    0    1
#> 
#> $VA[[2]]
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0
#> [4,]    0    0    0    1
#> 
#> $VA[[3]]
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0
#> [4,]    0    0    0    1
#> 
#> 


## ------------------------------------------------
## Method `specify_identification_bsvars$set_identification`
## ------------------------------------------------

spec = specify_identification_bsvars$new(N = 3, K = 4) # specify a model with the default option
B    = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
#>      [,1]  [,2]  [,3]
#> [1,] TRUE FALSE FALSE
#> [2,] TRUE FALSE  TRUE
#> [3,] TRUE  TRUE  TRUE
spec$set_identification(B = B, N = 3, K = 4)  # modify an existing specification
spec$get_identification()              # check the outcome
#> $VB
#> $VB[[1]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> 
#> $VB[[2]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> [2,]    0    0    1
#> 
#> $VB[[3]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> [2,]    0    1    0
#> [3,]    0    0    1
#> 
#> 
#> $VA
#> $VA[[1]]
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0
#> [4,]    0    0    0    1
#> 
#> $VA[[2]]
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0
#> [4,]    0    0    0    1
#> 
#> $VA[[3]]
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    0    0    0
#> [2,]    0    1    0    0
#> [3,]    0    0    1    0
#> [4,]    0    0    0    1
#> 
#>