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R6 Class Representing StartingValuesBSVAREXH

Source: R/specify_bsvar_exh.R
specify_starting_values_bsvar_exh.Rd

The class StartingValuesBSVAREXH presents starting values for the bsvar model with exogenous regime change Heteroskedasticity.

Super class

StartingValuesBSVAR -> StartingValuesBSVAREXH

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.

sigma2

an NxM matrix of starting values for the regime-specific variances of the structural shocks. Its elements sum to value M over the rows.

xi

an MxT matrix of starting values for the Markov process indicator. Its columns are a chosen column of an identity matrix of order M.

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

StartingValuesBSVAREXH$new()

Create new starting values StartingValuesBSVAREXH.

Usage

StartingValuesBSVAREXH$new(A, B, N, p, T, d = 0, variance_regimes = rep(1, T))

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 structural 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.

p

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

T

a positive integer - the the time series dimension of the dependent variable matrix \(Y\).

d

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

variance_regimes

a T-vector with exogenous regime indicators that are integer numbers associating the time observation with heteroskedastic regime.

Returns

Starting values StartingValuesBSVAREXH.

StartingValuesBSVAREXH$get_starting_values()

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

Usage

StartingValuesBSVAREXH$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_exh$new(A = A, B = B, N = 3, p = 1, T = 100)
sv$get_starting_values()   # show starting values as list

StartingValuesBSVAREXH$set_starting_values()

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

Usage

StartingValuesBSVAREXH$set_starting_values(last_draw)

Arguments

last_draw

a list containing the last draw.

Returns

An object of class StartingValuesBSVAREXH 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 bsvar model with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_exh$new(A = A, B = B, N = 3, p = 1, T = 100)

# 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

StartingValuesBSVAREXH$clone()

The objects of this class are cloneable with this method.

Usage

StartingValuesBSVAREXH$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# starting values for a bsvar model for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_exh$new(A = A, B = B, N = 3, p = 1, T = 100)


## ------------------------------------------------
## Method `StartingValuesBSVAREXH$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_exh$new(A = A, B = B, N = 3, p = 1, T = 100)
sv$get_starting_values()   # show starting values as list
#> $B
#>           [,1]      [,2]      [,3]
#> [1,] 0.7110222 0.0000000 0.0000000
#> [2,] 0.0000000 0.3600283 0.0000000
#> [3,] 0.0000000 0.0000000 0.1245518
#> 
#> $A
#>           [,1]     [,2]      [,3] [,4]
#> [1,] 0.8016087 0.000000 0.0000000    0
#> [2,] 0.0000000 0.765811 0.0000000    0
#> [3,] 0.0000000 0.000000 0.5481055    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]
#> [1,]     1      1
#> [2,]     1      1
#> [3,]     1      1
#> 
#> $df
#> [1] 3 3 3
#> 
#> $sigma2
#>      [,1]
#> [1,]    1
#> [2,]    1
#> [3,]    1
#> 
#> $xi
#>      [,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
#>      [,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
#>      [,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
#>      [,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
#>      [,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
#>      [,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
#>      [,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
#>      [,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
#>      [,99] [,100]
#> [1,]     1      1
#> 


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

# starting values for a bsvar model with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_exh$new(A = A, B = B, N = 3, p = 1, T = 100)

# 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