specify_bsvar_t: R6 Class representing the specification of the BSVAR model...

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

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

Description

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

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

• specify_bsvar_t$new()

• specify_bsvar_t$clone()

Inherited methods

• bsvars::BSVAR$get_data_matrices()
• bsvars::BSVAR$get_identification()
• bsvars::BSVAR$get_prior()
• bsvars::BSVAR$get_starting_values()

---

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.

See Also

estimate, specify_posterior_bsvar_t

Examples

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

bsvars documentation built on Oct. 24, 2024, 5:11 p.m.