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R6 Class representing the specification of the BSVARSIGN model

Source: R/specify.R
specify_bsvarSIGN.Rd

The class BSVARSIGN presents complete specification for the Bayesian Structural VAR model with sign and narrative restrictions.

See also

estimate.BSVARSIGN, specify_posterior_bsvarSIGN

Public fields

p

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

identification

an object IdentificationBSVARSIGN with the identifying restrictions.

prior

an object PriorBSVARSIGN with the prior specification.

data_matrices

an object DataMatricesBSVARSIGN with the data matrices.

starting_values

an object StartingValuesBSVARSIGN with the starting values.

num_foreign_vars

a non-negative integer specifying the number of foreign variables.

mc.cores

number of cores to use for parallel computing.

Methods

Public methods

Method new()

Create a new specification of the Bayesian Structural VAR model with sign and narrative restrictions BSVARSIGN.

Usage

specify_bsvarSIGN$new(
  data,
  p = 1L,
  sign_irf,
  sign_narrative,
  sign_structural,
  max_tries = Inf,
  exogenous = NULL,
  foreign = NULL,
  stationary = NULL,
  hyper_mu = TRUE,
  hyper_delta = TRUE,
  hyper_lambda = TRUE,
  hyper_psi = TRUE,
  hyper_covid = NULL,
  mc.cores = 1
)

Arguments

data

a (T+p)xN matrix with time series data.

p

a positive integer providing model's autoregressive lag order.

sign_irf

a NxNxH array - sign and zero restrictions on the impulse response functions, ±1 for positive/negative sign restriction 0 for zero restrictions and NA for no restrictions, the h-th slice NxN matrix contains the restrictions on the h-1 horizon.

sign_narrative

a list of objects of class "narrative" - narrative sign restrictions.

sign_structural

a NxN matrix with entries ±1 or NA - sign restrictions on the contemporaneous relations B between reduced-form errors E and structural shocks U where BE=U.

max_tries

a positive integer with the maximum number of iterations for finding a rotation matrix \(Q\) that would satisfy sign restrictions

exogenous

a (T+p)xd matrix of exogenous variables.

foreign

a matrix of foreign variables for a Small Open Economy (SOE) model. Defaults to NULL.

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.

hyper_mu

whether to estimate the hyper-parameter in the sum-of-coefficients dummy prior.

hyper_delta

whether to estimate the hyper-parameter in the single-unit-root dummy prior.

hyper_lambda

whether to estimate the hyper-parameter of the shrinkage in the Minnesota prior.

hyper_psi

whether to estimate the hyper-parameter of the variances in the Minnesota prior.

hyper_covid

NULL or positive integer indicating the start of the COVID-19 pandemic.

mc.cores

number of cores to use for parallel computing. Default is 1. We recommend setting it to parallel::detectCores() - 1.

Returns

A new complete specification for the Bayesian Structural VAR model BSVARSIGN.

Method get_data_matrices()

Returns the data matrices as the DataMatricesBSVAR object.

Usage

specify_bsvarSIGN$get_data_matrices()

Examples

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the data matrices
spec$get_data_matrices()

Method no_dummy_observations()

Sets the sum-of-coefficients and single-unit-root dummy observations to zero (removes the dummy observation prior).

Usage

specify_bsvarSIGN$no_dummy_observations()

Examples

# specify the model
data(optimism)
spec = specify_bsvarSIGN$new(optimism, p = 4)
spec$no_dummy_observations() # remove dummy observations

Method estimate_hyper()

Estimates hyper-parameters with adaptive Metropolis algorithm.

Usage

specify_bsvarSIGN$estimate_hyper(S = 10000, burn_in = S/2)

Arguments

S

number of MCMC draws.

burn_in

number of burn-in draws.

Examples

# specify the model and set seed
set.seed(123)
data(optimism)
spec = specify_bsvarSIGN$new(optimism, p = 4)

# estimate hyper parameters with adaptive Metropolis algorithm
spec$estimate_hyper(S = 10)

# trace plot
hyper = t(spec$prior$hyper)[, 4:8]
colnames(hyper) = paste("psi", 1:5, sep = "")
plot.ts(hyper)

Method get_identification()

Returns the identifying restrictions as the IdentificationBSVARSIGN object.

Usage

specify_bsvarSIGN$get_identification()

Examples

# specify a model with the optimism data and 4 lags
data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the identifying restrictions
spec$get_identification()

Method get_prior()

Returns the prior specification as the PriorBSVAR object.

Usage

specify_bsvarSIGN$get_prior()

Examples

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the prior specification
spec$get_prior()

Method get_starting_values()

Returns the starting values as the StartingValuesBSVAR object.

Usage

specify_bsvarSIGN$get_starting_values()

Examples

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the starting values
spec$get_starting_values()

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bsvarSIGN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# specify a model with the optimism data and 4 lags

data(optimism)
specification = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)


## ------------------------------------------------
## Method `specify_bsvarSIGN$get_data_matrices`
## ------------------------------------------------

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the data matrices
spec$get_data_matrices()
#> <DataMatricesBSVAR>
#>   Public:
#>     X: 0.200390767 -11.08316654 -4.301382328 0.012867114 -7.572 ...
#>     Y: 0.194524324 -11.02210681 -4.293947793 0.024503568 -7.577 ...
#>     clone: function (deep = FALSE) 
#>     get_data_matrices: function () 
#>     initialize: function (data, p = 1L, exogenous = NULL) 


## ------------------------------------------------
## Method `specify_bsvarSIGN$no_dummy_observations`
## ------------------------------------------------

# specify the model
data(optimism)
spec = specify_bsvarSIGN$new(optimism, p = 4)
spec$no_dummy_observations() # remove dummy observations


## ------------------------------------------------
## Method `specify_bsvarSIGN$estimate_hyper`
## ------------------------------------------------

# specify the model and set seed
set.seed(123)
data(optimism)
spec = specify_bsvarSIGN$new(optimism, p = 4)

# estimate hyper parameters with adaptive Metropolis algorithm
spec$estimate_hyper(S = 10)
#> **************************************************|
#>  Adaptive Metropolis MCMC: hyper parameters       |
#> **************************************************|

# trace plot
hyper = t(spec$prior$hyper)[, 4:8]
colnames(hyper) = paste("psi", 1:5, sep = "")
plot.ts(hyper)



## ------------------------------------------------
## Method `specify_bsvarSIGN$get_identification`
## ------------------------------------------------

# specify a model with the optimism data and 4 lags
data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the identifying restrictions
spec$get_identification()
#> <IdentificationBSVARSIGN>
#>   Public:
#>     VB: list
#>     clone: function (deep = FALSE) 
#>     get_identification: function () 
#>     initialize: function (N, sign_irf, sign_narrative, sign_structural, max_tries = Inf) 
#>     max_tries: Inf
#>     set_identification: function (N, sign_irf, sign_narrative, sign_structural) 
#>     sign_irf: NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA ...
#>     sign_narrative: list
#>     sign_structural: 1 NA NA NA NA NA 1 NA NA NA NA NA 1 NA NA NA NA NA 1 NA  ...


## ------------------------------------------------
## Method `specify_bsvarSIGN$get_prior`
## ------------------------------------------------

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the prior specification
spec$get_prior()
#> <PriorBSVARSIGN>
#>   Public:
#>     A: 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0  ...
#>     S: 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1
#>     V: 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0  ...
#>     X: 0.200390767 -11.08316654 -4.301382328 0.012867114 -7.572 ...
#>     Xsoc: 0.201757572 0 0 0 0 0.201757572 0 0 0 0 0.201757572 0 0  ...
#>     Xsur: 0.201757572 -11.0726542625 -4.2879895105 0.00226154775 - ...
#>     Y: 0.194524324 -11.02210681 -4.293947793 0.024503568 -7.577 ...
#>     Ysoc: 0.201757572 0 0 0 0 0 -11.0726542625 0 0 0 0 0 -4.287989 ...
#>     Ysur: 0.201757572 -11.0726542625 -4.2879895105 0.00226154775 - ...
#>     clone: function (deep = FALSE) 
#>     covid: NULL
#>     data: NA
#>     delta.scale: 0.618033988749895
#>     delta.shape: 2.61803398874989
#>     get_prior: function () 
#>     hyper: 1 1 0.2 6.63317874235789e-05 0.00655120654453227 1.68010 ...
#>     initialize: function (data, p, exogenous = NULL, stationary = rep(FALSE, 
#>     lambda.scale: 0.540312423743285
#>     lambda.shape: 1.37015621187164
#>     mu.scale: 0.618033988749895
#>     mu.shape: 2.61803398874989
#>     nu: 7
#>     p: 4
#>     psi.scale: 0.000799362037821841
#>     psi.shape: 0.998405094554603


## ------------------------------------------------
## Method `specify_bsvarSIGN$get_starting_values`
## ------------------------------------------------

# specify a model with the optimism data and 4 lags

data(optimism)
spec = specify_bsvarSIGN$new(
   data = optimism,
   p = 4
)

# get the starting values
spec$get_starting_values()
#> NULL