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R6 Class Representing StartingValuesBSVAR

Source: R/specify_bsvar.R
specify_starting_values_bsvar.Rd

The class StartingValuesBSVAR presents starting values for the homoskedastic bsvar model.

Public fields

A

an NxK matrix of starting values for the parameter \(A\).

B

an NxN matrix of starting values for the parameter \(B\).

hyper

a (2*N+1)x2 matrix of starting values for the shrinkage hyper-parameters of the hierarchical prior distribution.

lambda

a NxT matrix of starting values for latent variables.

df

an Nx1 vector of positive numbers with starting values for the equation-specific degrees of freedom parameters of the Student-t conditional distribution of structural shocks.

Methods

Public methods

StartingValuesBSVAR$new()

Create new starting values StartingValuesBSVAR.

Usage

StartingValuesBSVAR$new(A, B, N, T, p, d = 0)

Arguments

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.

B

a logical NxN matrix containing value TRUE for the elements of the staructural matrix \(B\) 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.

T

a positive integer - the number of time periods in the data.

p

a positive integer - the autoregressive lag order of the SVAR model.

d

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

Returns

Starting values StartingValuesBSVAR.

Examples

# starting values for a homoskedastic bsvar with 4 lags for a 3-variable system
A = matrix(TRUE, 3, 13)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar$new(A = A, B = B, N = 3, T = 120, p = 4)

StartingValuesBSVAR$get_starting_values()

Returns the elements of the starting values StartingValuesBSVAR as a list.

Usage

StartingValuesBSVAR$get_starting_values()

Examples

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar$new(A = A, B = B, N = 3, T = 120, p = 1)
sv$get_starting_values()   # show starting values as list

StartingValuesBSVAR$set_starting_values()

Returns the elements of the starting values StartingValuesBSVAR as a list.

Usage

StartingValuesBSVAR$set_starting_values(last_draw)

Arguments

last_draw

a list containing the last draw of elements B - an NxN matrix, A - an NxK matrix, and hyper - a vector of 5 positive real numbers.

Returns

An object of class StartingValuesBSVAR including the last draw of the current MCMC as the starting value to be passed to the continuation of the MCMC estimation using estimate().

Examples

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar$new(A = A, B = B, N = 3, T = 120, p = 1)

# Modify the starting values by:
sv_list = sv$get_starting_values()   # getting them as list
sv_list$A <- matrix(rnorm(12), 3, 4) # modifying the entry
sv$set_starting_values(sv_list)      # providing to the class object

StartingValuesBSVAR$clone()

The objects of this class are cloneable with this method.

Usage

StartingValuesBSVAR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# starting values for a homoskedastic bsvar for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar$new(A = A, B = B, N = 3, T = 120, p = 1)


## ------------------------------------------------
## Method `StartingValuesBSVAR$new()`
## ------------------------------------------------

# starting values for a homoskedastic bsvar with 4 lags for a 3-variable system
A = matrix(TRUE, 3, 13)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar$new(A = A, B = B, N = 3, T = 120, p = 4)


## ------------------------------------------------
## Method `StartingValuesBSVAR$get_starting_values()`
## ------------------------------------------------

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar$new(A = A, B = B, N = 3, T = 120, p = 1)
sv$get_starting_values()   # show starting values as list
#> $B
#>           [,1]      [,2]      [,3]
#> [1,] 0.2312103 0.0000000 0.0000000
#> [2,] 0.0000000 0.3272076 0.0000000
#> [3,] 0.0000000 0.0000000 0.2953724
#> 
#> $A
#>           [,1]      [,2]      [,3] [,4]
#> [1,] 0.4605645 0.0000000 0.0000000    0
#> [2,] 0.0000000 0.6038269 0.0000000    0
#> [3,] 0.0000000 0.0000000 0.5531581    0
#> 
#> $hyper
#>      [,1] [,2]
#> [1,]   10   10
#> [2,]   10   10
#> [3,]   10   10
#> [4,]   10   10
#> [5,]   10   10
#> [6,]   10   10
#> [7,]   10   10
#> 
#> $lambda
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]    1    1    1    1    1    1    1    1    1     1     1     1     1     1
#> [2,]    1    1    1    1    1    1    1    1    1     1     1     1     1     1
#> [3,]    1    1    1    1    1    1    1    1    1     1     1     1     1     1
#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [2,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [3,]     1     1     1     1     1     1     1     1     1     1     1     1
#>      [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [2,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [3,]     1     1     1     1     1     1     1     1     1     1     1     1
#>      [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
#> [1,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [2,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [3,]     1     1     1     1     1     1     1     1     1     1     1     1
#>      [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
#> [1,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [2,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [3,]     1     1     1     1     1     1     1     1     1     1     1     1
#>      [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
#> [1,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [2,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [3,]     1     1     1     1     1     1     1     1     1     1     1     1
#>      [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
#> [1,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [2,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [3,]     1     1     1     1     1     1     1     1     1     1     1     1
#>      [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98]
#> [1,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [2,]     1     1     1     1     1     1     1     1     1     1     1     1
#> [3,]     1     1     1     1     1     1     1     1     1     1     1     1
#>      [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108]
#> [1,]     1      1      1      1      1      1      1      1      1      1
#> [2,]     1      1      1      1      1      1      1      1      1      1
#> [3,]     1      1      1      1      1      1      1      1      1      1
#>      [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118]
#> [1,]      1      1      1      1      1      1      1      1      1      1
#> [2,]      1      1      1      1      1      1      1      1      1      1
#> [3,]      1      1      1      1      1      1      1      1      1      1
#>      [,119] [,120]
#> [1,]      1      1
#> [2,]      1      1
#> [3,]      1      1
#> 
#> $df
#> [1] 3 3 3
#> 


## ------------------------------------------------
## Method `StartingValuesBSVAR$set_starting_values()`
## ------------------------------------------------

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar$new(A = A, B = B, N = 3, T = 120, p = 1)

# Modify the starting values by:
sv_list = sv$get_starting_values()   # getting them as list
sv_list$A <- matrix(rnorm(12), 3, 4) # modifying the entry
sv$set_starting_values(sv_list)      # providing to the class object