specify_identification_bsvars: R6 Class Representing IdentificationBSVARs

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

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.

VA

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

Methods

Public methods

• IdentificationBSVARs$new()

• IdentificationBSVARs$get_identification()

• IdentificationBSVARs$set_identification()

• IdentificationBSVARs$clone()

---

IdentificationBSVARs$new()

Create new identifying restrictions IdentificationBSVARs.

Usage

IdentificationBSVARs$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.

---

IdentificationBSVARs$get_identification()

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

Usage

IdentificationBSVARs$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()

---

IdentificationBSVARs$set_identification()

Set new starting values StartingValuesBSVAR.

Usage

IdentificationBSVARs$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

---

IdentificationBSVARs$clone()

The objects of this class are cloneable with this method.

Usage

IdentificationBSVARs$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

B = matrix(c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), 3, 3); B
specify_identification_bsvars$new(B = B, N = 3, K = 4) # an alternative identification pattern


## ------------------------------------------------
## Method `IdentificationBSVARs$get_identification()`
## ------------------------------------------------

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 `IdentificationBSVARs$set_identification()`
## ------------------------------------------------

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

bsvars documentation built on Aug. 22, 2026, 5:09 p.m.