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R6 Class representing the specification of the BVARGROUPPANEL model

Source: R/specify_bvargrouppanel.R
specify_bvarGroupPANEL.Rd

The class BVARGROUPPANEL presents complete specification for the Bayesian Panel Vector Autoregression with county groups. The groups can be pre-specified, which requires the argument group_allocation to be provided, or estimated, which requires the argument G for the number of groups to be provided and the argument group_allocation to be left empty.

References

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

Super class

bpvars::BVARPANEL -> BVARGROUPPANEL

Public fields

p

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

G

a non-negative integer specifying the number of country groupings.

estimate_groups

a logical value denoting whether the groups are to be estimated.

prior

an object PriorBSVAR with the prior specification.

data_matrices

an object DataMatricesBVARPANEL with the data matrices.

starting_values

an object StartingValuesBVARGROUPPANEL 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

Inherited methods

Method new()

Create a new specification of the Bayesian Panel VAR model with country grouping BVARGROUPPANEL. The groups can be pre-specified, which requires the argument group_allocation to be provided, or estimated, which requires the argument G for the number of groups to be provided and the argument group_allocation to be left empty.

Usage

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

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.

G

a positive integer specifying the number of country groups. Its specification is required if group_allocation is not provided and the country groups to be estimated.

group_allocation

an argument that can be provided as a numeric vector with integer numbers denoting group allocations to pre-specify the the country groups, in which case they are not estimated, or left empty if the country groups are to be estimated.

Returns

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

Method set_global2pooled()

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

Usage

specify_bvarGroupPANEL$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_bvarGroupPANEL$new(
   data = ilo_dynamic_panel,
   p = 4,
   G = 2
)
spec$set_global2pooled()

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bvarGroupPANEL$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

data(ilo_dynamic_panel)
spec = specify_bvarGroupPANEL$new(
   data = ilo_dynamic_panel,
   p = 4,
   G = 2
)
#> Country groupings will be estimated.


## ------------------------------------------------
## Method `specify_bvarGroupPANEL$set_global2pooled`
## ------------------------------------------------

spec = specify_bvarGroupPANEL$new(
   data = ilo_dynamic_panel,
   p = 4,
   G = 2
)
#> Country groupings will be estimated.
spec$set_global2pooled()