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R6 Class representing the specification of the BVARGROUPPRIORPANEL model

Source: R/specify_bvargrouppriorpanel.R
specify_bvarGroupPriorPANEL.Rd

The class BVARGROUPPRIORPANEL presents complete specification for the Bayesian Panel Vector Autoregression with county grouping for global prior parameters. 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 .

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 StartingValuesBVARGROUPPRIORPANEL 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

BVARGROUPPRIORPANEL$new()

Create a new specification of the Bayesian Panel VAR model with country grouping for global prior parameters BVARGROUPPRIORPANEL. 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

BVARGROUPPRIORPANEL$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.

BVARGROUPPRIORPANEL$get_data_matrices()

Returns the data matrices as the DataMatricesBVARPANEL object.

Usage

BVARGROUPPRIORPANEL$get_data_matrices()

Examples

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

BVARGROUPPRIORPANEL$get_prior()

Returns the prior specification as the PriorBVARPANEL object.

Usage

BVARGROUPPRIORPANEL$get_prior()

Examples

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

BVARGROUPPRIORPANEL$get_starting_values()

Returns the starting values as the StartingValuesBVARPANEL object.

Usage

BVARGROUPPRIORPANEL$get_starting_values()

Examples

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

BVARGROUPPRIORPANEL$get_type()

Returns the type of the model.

Usage

BVARGROUPPRIORPANEL$get_type()

Examples

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

BVARGROUPPRIORPANEL$set_global2pooled()

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

Usage

BVARGROUPPRIORPANEL$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()

BVARGROUPPRIORPANEL$set_adaptiveMH()

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

Usage

BVARGROUPPRIORPANEL$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$new(
   data = ilo_dynamic_panel[1:5]
)
spec$set_adaptiveMH(c(0.6, 0.4, 10, 0.1))

BVARGROUPPRIORPANEL$clone()

The objects of this class are cloneable with this method.

Usage

BVARGROUPPRIORPANEL$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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


## ------------------------------------------------
## Method `BVARGROUPPRIORPANEL$get_data_matrices()`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_data_matrices()
#> <DataMatricesBVARPANEL>
#>   Public:
#>     Y: list
#>     clone: function (deep = FALSE) 
#>     exogenous: list
#>     get_data_matrices: function () 
#>     initialize: function (data, p = 1L, exogenous = NULL, type = rep("real", 
#>     missing: list
#>     type: real real real real


## ------------------------------------------------
## Method `BVARGROUPPRIORPANEL$get_prior()`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_prior()
#> <PriorBVARPANEL>
#>   Public:
#>     M: 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0
#>     S_Sigma_inv: 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1
#>     S_inv: 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1
#>     W: 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 10
#>     a_w: 1
#>     clone: function (deep = FALSE) 
#>     eta: 5
#>     get_prior: function () 
#>     initialize: function (C, N, p, d = 0, stationary = rep(FALSE, N)) 
#>     lambda: 72
#>     mu_Sigma: 5
#>     mu_m: 1
#>     nu_s: 3
#>     s_s: 1
#>     s_w: 1
#>     sigma2_m: 1


## ------------------------------------------------
## Method `BVARGROUPPRIORPANEL$get_starting_values()`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_starting_values()
#> <StartingValuesBVARPANEL>
#>   Public:
#>     A: 1.00116165770763 0.000278610576251996 0.0004603944288530 ...
#>     A_c: 0.000961575795264991 0.000465729787011991 0.000901217167 ...
#>     Sigma: 0.957294128849276 -0.894005328637003 0.611378256564174 0 ...
#>     Sigma_c: 2.73878583772208 -3.18613427139964 2.12132858657354 -3.1 ...
#>     V: 7.64473455219002 0.20870502349516 -0.709831326477997 -0. ...
#>     clone: function (deep = FALSE) 
#>     get_starting_values: function () 
#>     initialize: function (C, N, p, d = 0) 
#>     m: -0.000806155390666568
#>     nu: 5.1
#>     s: 0.0493133345701443
#>     set_starting_values: function (last_draw) 
#>     w: 1.97055002989595


## ------------------------------------------------
## Method `BVARGROUPPRIORPANEL$get_type()`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel
)
spec$get_type()
#> [1] "wozniak"


## ------------------------------------------------
## Method `BVARGROUPPRIORPANEL$set_global2pooled()`
## ------------------------------------------------

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


## ------------------------------------------------
## Method `BVARGROUPPRIORPANEL$set_adaptiveMH()`
## ------------------------------------------------

spec = specify_bvarPANEL$new(
   data = ilo_dynamic_panel[1:5]
)
spec$set_adaptiveMH(c(0.6, 0.4, 10, 0.1))