specify_bsvar_sv | R Documentation |
The class BSVARSV presents complete specification for the BSVAR model with Stochastic Volatility heteroskedasticity.
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.
new()
Create a new specification of the BSVAR model with Stochastic Volatility heteroskedasticity, BSVARSV.
specify_bsvar_sv$new( data, p = 1L, B, exogenous = NULL, centred_sv = FALSE, stationary = rep(FALSE, ncol(data)) )
data
a (T+p)xN
matrix with time series data.
p
a positive integer providing model's autoregressive lag order.
B
a logical NxN
matrix containing value TRUE
for the elements of the structural matrix B
to be estimated and value FALSE
for exclusion restrictions to be set to zero.
exogenous
a (T+p)xd
matrix of exogenous variables.
centred_sv
a logical value. If FALSE
a non-centred Stochastic Volatility processes for conditional variances are estimated. Otherwise, a centred process is estimated.
stationary
an N
logical vector - its element set to FALSE
sets the prior mean for the autoregressive parameters of the N
th equation to the white noise process, otherwise to random walk.
A new complete specification for the bsvar model with Stochastic Volatility heteroskedasticity, BSVARSV.
get_data_matrices()
Returns the data matrices as the DataMatricesBSVAR object.
specify_bsvar_sv$get_data_matrices()
data(us_fiscal_lsuw) spec = specify_bsvar_sv$new( data = us_fiscal_lsuw, p = 4 ) spec$get_data_matrices()
get_identification()
Returns the identifying restrictions as the IdentificationBSVARs object.
specify_bsvar_sv$get_identification()
data(us_fiscal_lsuw) spec = specify_bsvar_sv$new( data = us_fiscal_lsuw, p = 4 ) spec$get_identification()
get_prior()
Returns the prior specification as the PriorBSVARSV object.
specify_bsvar_sv$get_prior()
data(us_fiscal_lsuw) spec = specify_bsvar_sv$new( data = us_fiscal_lsuw, p = 4 ) spec$get_prior()
get_starting_values()
Returns the starting values as the StartingValuesBSVARSV object.
specify_bsvar_sv$get_starting_values()
data(us_fiscal_lsuw) spec = specify_bsvar_sv$new( data = us_fiscal_lsuw, p = 4 ) spec$get_starting_values()
clone()
The objects of this class are cloneable with this method.
specify_bsvar_sv$clone(deep = FALSE)
deep
Whether to make a deep clone.
estimate
, specify_posterior_bsvar_sv
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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