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R6 Class representing the specification of the BSVARHMSH model with Heterogeneous Markov Switching Heteroskedasticity.

Source: R/specify_bsvar_hmsh.R
specify_bsvar_hmsh.Rd

The class BSVARHMSH presents complete specification for the BSVAR model with Heterogeneous Markov Switching Heteroskedasticity.

See also

estimate, specify_posterior_bsvar_hmsh

Public fields

p

a non-negative integer specifying the autoregressive lag order of the model.

identification

an object IdentificationBSVARs with the identifying restrictions.

prior

an object PriorBSVARMSH with the prior specification.

data_matrices

an object DataMatricesBSVAR with the data matrices.

starting_values

an object StartingValuesBSVARHMSH with the starting values.

finiteM

a logical value - if true a stationary Markov switching model is estimated. Otherwise, a sparse Markov switching model is estimated in which M=20 and the number of visited states is estimated.

Methods

Public methods

Method new()

Create a new specification of the BSVAR model with Heterogeneous Markov Switching Heteroskedasticity, BSVARHMSH.

Usage

specify_bsvar_hmsh$new(
  data,
  p = 1L,
  M = 2L,
  B,
  A,
  distribution = c("norm", "t"),
  exogenous = NULL,
  stationary = rep(FALSE, ncol(data)),
  finiteM = TRUE
)

Arguments

data

a (T+p)xN matrix with time series data.

p

a positive integer providing model's autoregressive lag order.

M

an integer greater than 1 - the number of Markov process' heteroskedastic regimes.

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.

distribution

a character string specifying the conditional distribution of structural shocks. Value "norm" sets it to the normal distribution, while value "t" sets the Student-t distribution.

exogenous

a (T+p)xd matrix of exogenous variables.

stationary

an N logical vector - its element set to FALSE sets the prior mean for the autoregressive parameters of the Nth equation to the white noise process, otherwise to random walk.

finiteM

a logical value - if true a stationary Markov switching model is estimated. Otherwise, a sparse Markov switching model is estimated in which M=20 and the number of visited states is estimated.

Returns

A new complete specification for the bsvar model with Heterogeneous Markov Switching Heteroskedasticity, BSVARHMSH.

Method get_normal()

Returns the logical value of whether the conditional shock distribution is normal.

Usage

specify_bsvar_hmsh$get_normal()

Examples

spec = specify_bsvar_hmsh$new(us_fiscal_lsuw)
spec$get_normal()

Method get_data_matrices()

Returns the data matrices as the DataMatricesBSVAR object.

Usage

specify_bsvar_hmsh$get_data_matrices()

Examples

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_data_matrices()

Method get_identification()

Returns the identifying restrictions as the IdentificationBSVARs object.

Usage

specify_bsvar_hmsh$get_identification()

Examples

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_identification()

Method get_prior()

Returns the prior specification as the PriorBSVARMSH object.

Usage

specify_bsvar_hmsh$get_prior()

Examples

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_prior()

Method get_starting_values()

Returns the starting values as the StartingValuesBSVARHMSH object.

Usage

specify_bsvar_hmsh$get_starting_values()

Examples

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_starting_values()

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bsvar_hmsh$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
#> The identification is set to the default option of lower-triangular structural matrix.


## ------------------------------------------------
## Method `specify_bsvar_hmsh$get_normal`
## ------------------------------------------------

spec = specify_bsvar_hmsh$new(us_fiscal_lsuw)
#> The identification is set to the default option of lower-triangular structural matrix.
spec$get_normal()
#> [1] TRUE


## ------------------------------------------------
## Method `specify_bsvar_hmsh$get_data_matrices`
## ------------------------------------------------

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
#> The identification is set to the default option of lower-triangular structural matrix.
spec$get_data_matrices()
#> <DataMatricesBSVAR>
#>   Public:
#>     X: -10.6660327703266 -10.6935259109068 -8.41420587851191 -1 ...
#>     Y: -10.6725356416822 -10.6388624265757 -8.42993325328991 -1 ...
#>     clone: function (deep = FALSE) 
#>     get_data_matrices: function () 
#>     initialize: function (data, p = 1L, exogenous = NULL) 


## ------------------------------------------------
## Method `specify_bsvar_hmsh$get_identification`
## ------------------------------------------------

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
#> The identification is set to the default option of lower-triangular structural matrix.
spec$get_identification()
#> <IdentificationBSVARs>
#>   Public:
#>     VA: list
#>     VB: list
#>     clone: function (deep = FALSE) 
#>     get_identification: function () 
#>     initialize: function (B, A, N, K) 
#>     set_identification: function (B, A, N, K) 


## ------------------------------------------------
## Method `specify_bsvar_hmsh$get_prior`
## ------------------------------------------------

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
#> The identification is set to the default option of lower-triangular structural matrix.
spec$get_prior()
#> <PriorBSVARMSH>
#>   Inherits from: <PriorBSVAR>
#>   Public:
#>     A: 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  ...
#>     A_V_inv: 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0  ...
#>     B_V_inv: 1 0 0 0 1 0 0 0 1
#>     B_nu: 3
#>     PR_TR: 1 1 1 1
#>     clone: function (deep = FALSE) 
#>     get_prior: function () 
#>     hyper_a_A: 10
#>     hyper_a_B: 10
#>     hyper_nu_A: 10
#>     hyper_nu_AA: 10
#>     hyper_nu_B: 10
#>     hyper_nu_BB: 1
#>     hyper_s_AA: 10
#>     hyper_s_BB: 100
#>     initialize: function (N, p, d = 0, M, stationary = rep(FALSE, N)) 
#>     sigma_nu: 3
#>     sigma_s: 1


## ------------------------------------------------
## Method `specify_bsvar_hmsh$get_starting_values`
## ------------------------------------------------

spec = specify_bsvar_hmsh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
#> The identification is set to the default option of lower-triangular structural matrix.
spec$get_starting_values()
#> <StartingValuesBSVARHMSH>
#>   Inherits from: <StartingValuesBSVAR>
#>   Public:
#>     A: 0.829528798116371 0 0 0 0.0821833016816527 0 0 0 0.58130 ...
#>     B: 0.321282554650679 0 0 0 0.45038737799041 0 0 0 0.8370949 ...
#>     PR_TR: 1 0 0 1 1 0 0 1 1 0 0 1
#>     clone: function (deep = FALSE) 
#>     df: 3 3 3
#>     get_starting_values: function () 
#>     hyper: 10 10 10 10 10 10 10 10 10 10 10 10 10 10
#>     initialize: function (A, B, N, p, M, T, d = 0, finiteM = TRUE) 
#>     lambda: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1  ...
#>     pi_0: 0.5 0.5 0.5 0.5 0.5 0.5
#>     set_starting_values: function (last_draw) 
#>     sigma2: 1 1 1 1 1 1
#>     xi: 1 0 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 0  ...