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Forecasting using Structural Vector Autoregression

Source: R/forecast.R
forecast.PosteriorBSVARSIGN.Rd

Samples from the joint predictive density of all of the dependent variables for models from packages bsvars, bsvarSIGNs or bvarPANELs at forecast horizons from 1 to horizon specified as an argument of the function. Also facilitates forecasting using models with exogenous variables and conditional forecasting given projected future trajcetories of (some of the) variables.

Usage

# S3 method for class 'PosteriorBSVARSIGN'
forecast(
  posterior,
  horizon = 1,
  exogenous_forecast = NULL,
  conditional_forecast = NULL
)

Arguments

posterior

posterior estimation outcome - an object of class PosteriorBSVARSIGN obtained by running the estimate function.

horizon

a positive integer, specifying the forecasting horizon.

exogenous_forecast

a matrix of dimension horizon x d containing forecasted values of the exogenous variables.

conditional_forecast

a horizon x N matrix with forecasted values for selected variables. It should only contain numeric or NA values. The entries with NA values correspond to the values that are forecasted conditionally on the realisations provided as numeric values.

Value

A list of class Forecasts containing the draws from the predictive density and data. The output list includes element:

forecasts

an NxhorizonxS array with the draws from predictive density

Y

an \(NxT\) matrix with the data on dependent variables

See also

estimate.BSVARSIGN, summary, plot

Author

Tomasz Woźniak wozniak.tom@pm.me and Xiaolei Wang adamwang15@gmail.com

Examples

# upload data
data(optimism)

# specify the model and set seed
set.seed(123)

# + no effect on productivity (zero restriction)
# + positive effect on stock prices (positive sign restriction) 
sign_irf       = matrix(c(0, 1, rep(NA, 23)), 5, 5)
specification  = specify_bsvarSIGN$new(optimism, sign_irf = sign_irf)

# estimate the model
posterior      = estimate(specification, 10)
#> **************************************************|
#>  bsvarSIGNs: Bayesian Structural VAR with sign,   |
#>              zero and narrative restrictions      |
#> **************************************************|
#>  Progress of simulation for 10 independent draws
#>  Press Esc to interrupt the computations
#> **************************************************|

# sample from predictive density 1 year ahead
predictive     = forecast(posterior, 4)

# workflow with the pipe |>
############################################################
set.seed(123)
optimism |>
  specify_bsvarSIGN$new(sign_irf = sign_irf) |>
  estimate(S = 20) |> 
  forecast(horizon = 4) -> predictive
#> **************************************************|
#>  bsvarSIGNs: Bayesian Structural VAR with sign,   |
#>              zero and narrative restrictions      |
#> **************************************************|
#>  Progress of simulation for 20 independent draws
#>  Press Esc to interrupt the computations
#> **************************************************|

# conditional forecasting 2 quarters ahead conditioning on 
#  provided future values for the Gross Domestic Product 
############################################################
cf         = matrix(NA , 2, 5)
# # conditional forecasts equal to the last consumption observation
cf[,3]     = tail(optimism, 1)[3]
predictive = forecast(posterior, 2, conditional_forecast = cf)

# workflow with the pipe |>
############################################################
set.seed(123)
optimism |>
  specify_bsvarSIGN$new(sign_irf = sign_irf) |>
  estimate(S = 10) |> 
  forecast(horizon = 2, conditional_forecast = cf) -> predictive
#> **************************************************|
#>  bsvarSIGNs: Bayesian Structural VAR with sign,   |
#>              zero and narrative restrictions      |
#> **************************************************|
#>  Progress of simulation for 10 independent draws
#>  Press Esc to interrupt the computations
#> **************************************************|