bpvars (version 1.0)

specify_bvars: R6 Class representing the specification of the BVARs model

Description

The class BVARs presents complete specification for the Bayesian Vector Autoregressions for cubic data.

Public fields

p

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

prior

an object PriorBSVAR with the prior specification.

data_matrices

an object DataMatricesBVARPANEL with the data matrices.

starting_values

an object StartingValuesBVARPANEL with the starting values.

adaptiveMH

a vector of four values setting the adaptive MH sampler for nu: adaptive rate, target acceptance rate, the iteration at which to start adapting, the initial scaling rate

Methods

Public methods

• specify_bvars$new()

• specify_bvars$get_data_matrices()

• specify_bvars$get_prior()

• specify_bvars$get_starting_values()

• specify_bvars$get_type()

• specify_bvars$set_prior2objective()

• specify_bvars$set_global2pooled()

• specify_bvars$set_adaptiveMH()

• specify_bvars$clone()

---

Method new()

Create a new specification of the Bayesian Panel VAR model BVARPANEL.

Usage

specify_bvars$new(
  data,
  p = 1L,
  exogenous = NULL,
  stationary = rep(FALSE, ncol(data[[1]])),
  type = rep("real", ncol(data[[1]]))
)

Arguments

data

a list with C elements of (T_c+p)xN matrices with time series data.

p

a positive integer providing model's autoregressive lag order.

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.

type

an N character vector with elements set to "rate" or "real" determining the truncation of the predictive density to [0, 100] and (-Inf, Inf) (no truncation) for each of the variables.

Returns

A new complete specification for the Bayesian Panel VAR model BVARPANEL.

---

Method get_data_matrices()

Returns the data matrices as the DataMatricesBVARPANEL object.

Usage

specify_bvars$get_data_matrices()

Examples

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_data_matrices()

---

Method get_prior()

Returns the prior specification as the PriorBVARPANEL object.

Usage

specify_bvars$get_prior()

Examples

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_prior()

---

Method get_starting_values()

Returns the starting values as the StartingValuesBVARPANEL object.

Usage

specify_bvars$get_starting_values()

Examples

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_starting_values()

---

Method get_type()

Returns the type of the model.

Usage

specify_bvars$get_type()

Examples

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_type()

---

Method set_prior2objective()

Sets the VAR model priors to objective prior by Zellner (1972).

Usage

specify_bvars$set_prior2objective()

Examples

spec = specify_bvars$new(
   data = ilo_dynamic_panel
)
spec$set_prior2objective()

---

Method set_global2pooled()

Sets the prior mean of the global autoregressive parameters to the OLS pooled panel estimator following Zellner, Hong (1989).

Usage

specify_bvars$set_global2pooled(x)

Arguments

x

a vector of four values setting the adaptive MH sampler for nu: adaptive rate, target acceptance rate, the iteration at which to start adapting, the initial scaling rate

Examples

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$set_global2pooled()

---

Method set_adaptiveMH()

Sets the parameters of adaptive Metropolis-Hastings sampler for the parameter nu.

Usage

specify_bvars$set_adaptiveMH(x)

Arguments

x

a vector of four values setting the adaptive MH sampler for nu: adaptive rate, target acceptance rate, the iteration at which to start adapting, the initial scaling rate

Examples

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$set_adaptiveMH(c(0.6, 0.4, 10, 0.1))

---

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bvars$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Zellner (1971). An Introduction to Bayesian Inference in Econometrics. John Wiley & Sons.

Zellner, Hong (1989). Forecasting international growth rates using Bayesian shrinkage and other procedures. Journal of Econometrics, 40(1), 183–202, tools:::Rd_expr_doi("10.1016/0304-4076(89)90036-5").

Examples

spec = specify_bvars$new(
   data = ilo_dynamic_panel
)


## ------------------------------------------------
## Method `specify_bvars$get_data_matrices`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_data_matrices()


## ------------------------------------------------
## Method `specify_bvars$get_prior`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_prior()


## ------------------------------------------------
## Method `specify_bvars$get_starting_values`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_starting_values()


## ------------------------------------------------
## Method `specify_bvars$get_type`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_type()


## ------------------------------------------------
## Method `specify_bvars$set_prior2objective`
## ------------------------------------------------

spec = specify_bvars$new(
   data = ilo_dynamic_panel
)
spec$set_prior2objective()


## ------------------------------------------------
## Method `specify_bvars$set_global2pooled`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$set_global2pooled()


## ------------------------------------------------
## Method `specify_bvars$set_adaptiveMH`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$set_adaptiveMH(c(0.6, 0.4, 10, 0.1))