R6 Class Representing IdentificationBSVARs

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 \(B\).

Methods

Public methods

• specify_identification_bsvars$new()

• specify_identification_bsvars$get_identification()

• specify_identification_bsvars$set_identification()

• specify_identification_bsvars$clone()

---

Method new()

Create new identifying restrictions IdentificationBSVARs.

Usage

specify_identification_bsvars$new(N, B)

Arguments

N

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

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.

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$new(N = 3, B = B)
spec$get_identification()

---

Method set_identification()

Set new starting values StartingValuesBSVAR.

Usage

specify_identification_bsvars$set_identification(N, B)

Arguments

N

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

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.

Examples

spec = specify_identification_bsvars$new(N = 3) # specify a model with the default option
B    = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
spec$set_identification(N = 3, B = B)  # 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) # recursive specification for a 3-variable system
#> <IdentificationBSVARs>
#>   Public:
#>     VB: list
#>     clone: function (deep = FALSE) 
#>     get_identification: function () 
#>     initialize: function (N, B) 
#>     set_identification: function (N, B) 

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(N = 3, B = B) # an alternative identification pattern
#> <IdentificationBSVARs>
#>   Public:
#>     VB: list
#>     clone: function (deep = FALSE) 
#>     get_identification: function () 
#>     initialize: function (N, B) 
#>     set_identification: function (N, B) 


## ------------------------------------------------
## 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$new(N = 3, B = B)
spec$get_identification()
#> [[1]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> 
#> [[2]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> [2,]    0    0    1
#> 
#> [[3]]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> [2,]    0    1    0
#> [3,]    0    0    1
#> 


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

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