specify_identification_bsvars {bsvars}R Documentation

R6 Class Representing IdentificationBSVARs

Description

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

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

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
[Package bsvars version 2.1.0 Index]