bsvarSIGNs (version 2.0)

specify_bsvarSIGN: R6 Class representing the specification of the BSVARSIGN model

Description

The class BSVARSIGN presents complete specification for the Bayesian Structural VAR model with sign and narrative restrictions.

Public fields

p

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

identification

an object IdentificationBSVARSIGN with the identifying restrictions.

prior

an object PriorBSVARSIGN with the prior specification.

data_matrices

an object DataMatricesBSVARSIGN with the data matrices.

starting_values

an object StartingValuesBSVARSIGN with the starting values.

Methods

Public methods

• specify_bsvarSIGN$new()

• specify_bsvarSIGN$get_data_matrices()

• specify_bsvarSIGN$get_identification()

• specify_bsvarSIGN$get_prior()

• specify_bsvarSIGN$get_starting_values()

• specify_bsvarSIGN$clone()

---

Method new()

Create a new specification of the Bayesian Structural VAR model with sign and narrative restrictions BSVARSIGN.

Usage

specify_bsvarSIGN$new(
  data,
  p = 1L,
  sign_irf,
  sign_narrative,
  sign_structural,
  max_tries = Inf,
  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.

sign_irf

a NxNxH array - sign and zero restrictions on the impulse response functions, ±1 for positive/negative sign restriction 0 for zero restrictions and NA for no restrictions, the h-th slice NxN matrix contains the restrictions on the h-1 horizon.

sign_narrative

a list of objects of class "narrative" - narrative sign restrictions.

sign_structural

a NxN matrix with entries ±1 or NA - sign restrictions on the contemporaneous relations B between reduced-form errors E and structural shocks U where BE=U.

max_tries

a positive integer with the maximum number of iterations for finding a rotation matrix \(Q\) that would satisfy sign restrictions

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 Bayesian Structural VAR model BSVARSIGN.

---

Method get_data_matrices()

Returns the data matrices as the DataMatricesBSVAR object.

Usage

specify_bsvarSIGN$get_data_matrices()

Examples

# specify a model with the optimism data and 4 lags

data(optimism) spec = specify_bsvarSIGN$new( data = optimism, p = 4 )

# get the data matrices spec$get_data_matrices()

---

Method get_identification()

Returns the identifying restrictions as the IdentificationBSVARSIGN object.

Usage

specify_bsvarSIGN$get_identification()

Examples

# specify a model with the optimism data and 4 lags
data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the identifying restrictions spec$get_identification()

---

Method get_prior()

Returns the prior specification as the PriorBSVAR object.

Usage

specify_bsvarSIGN$get_prior()

Examples

# specify a model with the optimism data and 4 lags

data(optimism) spec = specify_bsvarSIGN$new( data = optimism, p = 4 )

# get the prior specification spec$get_prior()

---

Method get_starting_values()

Returns the starting values as the StartingValuesBSVAR object.

Usage

specify_bsvarSIGN$get_starting_values()

Examples

# specify a model with the optimism data and 4 lags

data(optimism) spec = specify_bsvarSIGN$new( data = optimism, p = 4 )

# get the starting values spec$get_starting_values()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bsvarSIGN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

estimate.BSVARSIGN, specify_posterior_bsvarSIGN

Examples

# specify a model with the optimism data and 4 lags

data(optimism)
specification = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)


## ------------------------------------------------
## Method `specify_bsvarSIGN$get_data_matrices`
## ------------------------------------------------

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the data matrices
spec$get_data_matrices()


## ------------------------------------------------
## Method `specify_bsvarSIGN$get_identification`
## ------------------------------------------------

# specify a model with the optimism data and 4 lags
data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the identifying restrictions
spec$get_identification()


## ------------------------------------------------
## Method `specify_bsvarSIGN$get_prior`
## ------------------------------------------------

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the prior specification
spec$get_prior()


## ------------------------------------------------
## Method `specify_bsvarSIGN$get_starting_values`
## ------------------------------------------------

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the starting values
spec$get_starting_values()