specify_bsvar_msh function

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

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

Examples

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

## ------------------------------------------------
## Method `specify_bsvar_msh$get_data_matrices`
## ------------------------------------------------

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

## ------------------------------------------------
## Method `specify_bsvar_msh$get_identification`
## ------------------------------------------------

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

## ------------------------------------------------
## Method `specify_bsvar_msh$get_prior`
## ------------------------------------------------

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

## ------------------------------------------------
## Method `specify_bsvar_msh$get_starting_values`
## ------------------------------------------------

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

See Also

estimate, specify_posterior_bsvar_msh

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 StartingValuesBSVARMSH 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

• list("specify_bsvar_msh$new()")

• list("specify_bsvar_msh$get_data_matrices()")

• list("specify_bsvar_msh$get_identification()")

• list("specify_bsvar_msh$get_prior()")

• list("specify_bsvar_msh$get_starting_values()")

• list("specify_bsvar_msh$clone()")

Method new()

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

Usage

specify_bsvar_msh$new(
  data,
  p = 1L,
  M = 2L,
  B,
  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.

• 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 Markov Switching Heteroskedasticity, BSVARMSH.

Method get_data_matrices()

Returns the data matrices as the DataMatricesBSVAR object.

Usage

specify_bsvar_msh$get_data_matrices()

Examples

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

Method get_identification()

Returns the identifying restrictions as the IdentificationBSVARs object.

Usage

specify_bsvar_msh$get_identification()

Examples

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

Method get_prior()

Returns the prior specification as the PriorBSVARMSH object.

Usage

specify_bsvar_msh$get_prior()

Examples

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

Method get_starting_values()

Returns the starting values as the StartingValuesBSVARMSH object.

Usage

specify_bsvar_msh$get_starting_values()

Examples

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

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bsvar_msh$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.