specify_bsvarSIGN {bsvarSIGNs} | R Documentation |
The class BSVARSIGN presents complete specification for the Bayesian Structural VAR model with sign and narrative restrictions.
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.
new()
Create a new specification of the Bayesian Structural VAR model with sign and narrative restrictions BSVARSIGN.
specify_bsvarSIGN$new( data, p = 1L, sign_irf, sign_narrative, sign_structural, max_tries = Inf, exogenous = NULL, stationary = rep(FALSE, ncol(data)) )
data
a (T+p)xN
matrix with time series data.
p
a positive integer providing model's autoregressive lag order.
sign_irf
a NxNxH
array - sign and zero restrictions
on the impulse response functions, ±1 for positive/negative sign restriction
0 for zero restrictions and NA for no restrictions,
the h
-th slice NxN
matrix contains the
restrictions on the h-1
horizon.
sign_narrative
a list of objects of class "narrative" - narrative sign restrictions.
sign_structural
a NxN
matrix with entries ±1 or NA - sign restrictions on the
contemporaneous relations B
between reduced-form errors E
and
structural shocks U
where BE=U
.
max_tries
a positive integer with the maximum number of iterations
for finding a rotation matrix Q
that would satisfy sign restrictions
exogenous
a (T+p)xd
matrix of exogenous variables.
stationary
an N
logical vector - its element set to FALSE
sets
the prior mean for the autoregressive parameters of the N
th equation to the white noise process,
otherwise to random walk.
A new complete specification for the Bayesian Structural VAR model BSVARSIGN.
get_data_matrices()
Returns the data matrices as the DataMatricesBSVAR object.
specify_bsvarSIGN$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()
get_identification()
Returns the identifying restrictions as the IdentificationBSVARSIGN object.
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$get_identification()
get_prior()
Returns the prior specification as the PriorBSVAR object.
specify_bsvarSIGN$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()
get_starting_values()
Returns the starting values as the StartingValuesBSVAR object.
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$get_starting_values()
clone()
The objects of this class are cloneable with this method.
specify_bsvarSIGN$clone(deep = FALSE)
deep
Whether to make a deep clone.
estimate.BSVARSIGN
, specify_posterior_bsvarSIGN
# 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()