Learn R Programming
bsvars (version 3.1)

specify_identification_bsvars: R6 Class Representing IdentificationBSVARs

Description

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

Arguments

Public fields

VB

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

Methods

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

Run this code
specify_identification_bsvars$new(N = 3) # recursive specification for a 3-variable system

B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
specify_identification_bsvars$new(N = 3, B = B) # an alternative identification pattern


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

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

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

Run the code above in your browser using DataLab