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R6 Class representing the specification of the BSVARSIGN model

Source: R/specify.R
specify_bsvarSIGN.Rd

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

See also

estimate.BSVARSIGN, specify_posterior_bsvarSIGN

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

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.

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()
#> <DataMatricesBSVAR>
#>   Public:
#>     X: 0.200390767 -11.08316654 -4.301382328 0.012867114 -7.572 ...
#>     Y: 0.194524324 -11.02210681 -4.293947793 0.024503568 -7.577 ...
#>     clone: function (deep = FALSE) 
#>     get_data_matrices: function () 
#>     initialize: function (data, p = 1L, exogenous = NULL) 


## ------------------------------------------------
## 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()
#> <IdentificationBSVARSIGN>
#>   Public:
#>     VB: list
#>     clone: function (deep = FALSE) 
#>     get_identification: function () 
#>     initialize: function (N, sign_irf, sign_narrative, sign_structural, max_tries = Inf) 
#>     max_tries: Inf
#>     set_identification: function (N, sign_irf, sign_narrative, sign_structural) 
#>     sign_irf: NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA ...
#>     sign_narrative: list
#>     sign_structural: 1 NA NA NA NA NA 1 NA NA NA NA NA 1 NA NA NA NA NA 1 NA  ...


## ------------------------------------------------
## 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()
#> <PriorBSVARSIGN>
#>   Public:
#>     A: 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0  ...
#>     S: 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1
#>     V: 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0  ...
#>     X: 0.200390767 -11.08316654 -4.301382328 0.012867114 -7.572 ...
#>     Xsoc: 0.201757572 0 0 0 0 0.201757572 0 0 0 0 0.201757572 0 0  ...
#>     Xsur: 0.201757572 -11.0726542625 -4.2879895105 0.00226154775 - ...
#>     Y: 0.194524324 -11.02210681 -4.293947793 0.024503568 -7.577 ...
#>     Ysoc: 0.201757572 0 0 0 0 0 -11.0726542625 0 0 0 0 0 -4.287989 ...
#>     Ysur: 0.201757572 -11.0726542625 -4.2879895105 0.00226154775 - ...
#>     clone: function (deep = FALSE) 
#>     data: NA
#>     delta.scale: 0.618033988749895
#>     delta.shape: 2.61803398874989
#>     estimate_hyper: function (S = 10000, burn_in = S/2, mu = FALSE, delta = FALSE, 
#>     get_prior: function () 
#>     hyper: 1 1 0.2 6.63317874235789e-05 0.00655120654453227 1.68010 ...
#>     initialize: function (data, p, exogenous = NULL, stationary = rep(FALSE, 
#>     lambda.scale: 0.540312423743285
#>     lambda.shape: 1.37015621187164
#>     mu.scale: 0.618033988749895
#>     mu.shape: 2.61803398874989
#>     nu: 7
#>     p: 4
#>     psi.scale: 0.000799362037821841
#>     psi.shape: 0.998405094554603


## ------------------------------------------------
## 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()
#> <StartingValuesBSVAR>
#>   Public:
#>     A: 0.492827306268737 0 0 0 0 0 0.616342768538743 0 0 0 0 0  ...
#>     B: 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1
#>     clone: function (deep = FALSE) 
#>     get_starting_values: function () 
#>     hyper: 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 ...
#>     initialize: function (N, p, d = 0) 
#>     set_starting_values: function (last_draw)