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bsvars (version 3.1)

specify_bsvar_sv: R6 Class representing the specification of the BSVAR model with Stochastic Volatility heteroskedasticity.

Description

The class BSVARSV presents complete specification for the BSVAR model with Stochastic Volatility heteroskedasticity.

Arguments

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 PriorBSVARSV with the prior specification.

data_matrices

an object DataMatricesBSVAR with the data matrices.

starting_values

an object StartingValuesBSVARSV with the starting values.

centred_sv

a logical value - if true a centred parameterisation of the Stochastic Volatility process is estimated. Otherwise, its non-centred parameterisation is estimated. See Lütkepohl, Shang, Uzeda, Woźniak (2022) for more info.

Methods

Method new()

Create a new specification of the BSVAR model with Stochastic Volatility heteroskedasticity, BSVARSV.

Usage

specify_bsvar_sv$new(
  data,
  p = 1L,
  B,
  exogenous = NULL,
  centred_sv = FALSE,
  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.

centred_sv

a logical value. If FALSE a non-centred Stochastic Volatility processes for conditional variances are estimated. Otherwise, a centred process is estimated.

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 bsvar model with Stochastic Volatility heteroskedasticity, BSVARSV.

Method get_data_matrices()

Returns the data matrices as the DataMatricesBSVAR object.

Usage

specify_bsvar_sv$get_data_matrices()

Examples

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

Method get_identification()

Returns the identifying restrictions as the IdentificationBSVARs object.

Usage

specify_bsvar_sv$get_identification()

Examples

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

Method get_prior()

Returns the prior specification as the PriorBSVARSV object.

Usage

specify_bsvar_sv$get_prior()

Examples

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

Method get_starting_values()

Returns the starting values as the StartingValuesBSVARSV object.

Usage

specify_bsvar_sv$get_starting_values()

Examples

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

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bsvar_sv$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

estimate, specify_posterior_bsvar_sv

Examples

Run this code
data(us_fiscal_lsuw)
spec = specify_bsvar_sv$new(
   data = us_fiscal_lsuw,
   p = 4
)


## ------------------------------------------------
## Method `specify_bsvar_sv$get_data_matrices`
## ------------------------------------------------

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


## ------------------------------------------------
## Method `specify_bsvar_sv$get_identification`
## ------------------------------------------------

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


## ------------------------------------------------
## Method `specify_bsvar_sv$get_prior`
## ------------------------------------------------

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


## ------------------------------------------------
## Method `specify_bsvar_sv$get_starting_values`
## ------------------------------------------------

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

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