specify_bsvar_t function

R6 Class representing the specification of the BSVAR model with t-distributed structural shocks.

The class BSVART presents complete specification for the BSVAR model with t-distributed structural shocks.

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar_t$new(
   data = us_fiscal_lsuw,
   p = 4
)

See Also

estimate, specify_posterior_bsvar_t

Super class

bsvars::BSVAR -> BSVART

Public fields

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

• identification: an object IdentificationBSVARs with the identifying restrictions.

• prior: an object PriorBSVART with the prior specification.

• data_matrices: an object DataMatricesBSVAR with the data matrices.

• starting_values: an object StartingValuesBSVART with the starting values.

• adaptiveMH: a vector of two values setting the Robust Adaptive Metropolis sampler for df: target acceptance rate and adaptive rate.

Methods

Public methods

• list("specify_bsvar_t$new()")

• list("specify_bsvar_t$clone()")

Method new()

Create a new specification of the BSVAR model with t-distributed structural shocks, BSVART.

Usage

specify_bsvar_t$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 bsvar model with t-distributed structural shocks, BSVART.

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bsvar_t$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.