specify_bsvar: R6 Class representing the specification of the homoskedastic...

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

R6 Class representing the specification of the homoskedastic BSVAR model

Description

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

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

• specify_bsvar$new()

• specify_bsvar$get_data_matrices()

• specify_bsvar$get_identification()

• specify_bsvar$get_prior()

• specify_bsvar$get_starting_values()

• 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.

See Also

estimate, specify_posterior_bsvar

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()

bsvars documentation built on Oct. 24, 2024, 5:11 p.m.