specify_bsvar function

R6 Class representing the specification of the homoskedastic BSVAR model

The class BSVAR presents complete specification for the homoskedastic bsvar model.

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)

## ------------------------------------------------
## Method `specify_bsvar$get_data_matrices`
## ------------------------------------------------

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)
spec$get_data_matrices()

## ------------------------------------------------
## Method `specify_bsvar$get_identification`
## ------------------------------------------------

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)
spec$get_identification()

## ------------------------------------------------
## Method `specify_bsvar$get_prior`
## ------------------------------------------------

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)
spec$get_prior()

## ------------------------------------------------
## Method `specify_bsvar$get_starting_values`
## ------------------------------------------------

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)
spec$get_starting_values()

See Also

estimate, specify_posterior_bsvar

Public fields

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

• identification: an object IdentificationBSVAR with the identifying restrictions.

• prior: an object PriorBSVAR with the prior specification.

• data_matrices: an object DataMatricesBSVAR with the data matrices.

• starting_values: an object StartingValuesBSVAR with the starting values.

Methods

Public methods

• list("specify_bsvar$new()")

• list("specify_bsvar$get_data_matrices()")

• list("specify_bsvar$get_identification()")

• list("specify_bsvar$get_prior()")

• list("specify_bsvar$get_starting_values()")

• list("specify_bsvar$clone()")

Method new()

Create a new specification of the homoskedastic bsvar model BSVAR.

Usage

specify_bsvar$new(
  data,
  p = 1L,
  B,
  exogenous = NULL,
  stationary = rep(FALSE, ncol(data))
)

Arguments

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

• p: a positive integer providing model's autoregressive lag order.

• 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.

Returns

A new complete specification for the homoskedastic bsvar model BSVAR.

Method get_data_matrices()

Returns the data matrices as the DataMatricesBSVAR object.

Usage

specify_bsvar$get_data_matrices()

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)
spec$get_data_matrices()

Method get_identification()

Returns the identifying restrictions as the IdentificationBSVARs object.

Usage

specify_bsvar$get_identification()

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)
spec$get_identification()

Method get_prior()

Returns the prior specification as the PriorBSVAR object.

Usage

specify_bsvar$get_prior()

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)
spec$get_prior()

Method get_starting_values()

Returns the starting values as the StartingValuesBSVAR object.

Usage

specify_bsvar$get_starting_values()

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar$new(
   data = us_fiscal_lsuw,
   p = 4
)
spec$get_starting_values()

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bsvar$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.