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.
Usage
# S3 method for class 'PosteriorBSVART'
forecast(
posterior,
horizon = 1,
exogenous_forecast = NULL,
conditional_forecast = NULL
)Arguments
- posterior
posterior estimation outcome - an object of class
PosteriorBSVARTobtained by running theestimatefunction.- horizon
a positive integer, specifying the forecasting horizon.
- exogenous_forecast
a matrix of dimension
horizon x dcontaining forecasted values of the exogenous variables.- conditional_forecast
a
horizon x Nmatrix with forecasted values for selected variables. It should only containnumericorNAvalues. The entries withNAvalues correspond to the values that are forecasted conditionally on the realisations provided asnumericvalues.
Value
A list of class Forecasts containing the
draws from the predictive density and data. The output list includes element:
- forecasts
an
NxTxSarray with the draws from predictive density- Y
an \(NxT\) matrix with the data on dependent variables
Author
Tomasz Woźniak wozniak.tom@pm.me
Examples
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
set.seed(123)
specification = specify_bsvar_t$new(us_fiscal_lsuw, p = 1)
#> The identification is set to the default option of lower-triangular structural matrix.
# run the burn-in
burn_in = estimate(specification, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> with t-distributed structural skocks |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# estimate the model
posterior = estimate(burn_in, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> with t-distributed structural skocks |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# sample from predictive density 1 year ahead
predictive = forecast(posterior, 4)
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar_t$new(p = 1) |>
estimate(S = 5) |>
estimate(S = 10) |>
forecast(horizon = 4) -> predictive
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> with t-distributed structural skocks |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> with t-distributed structural skocks |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# conditional forecasting using a model with exogenous variables
############################################################
data(us_fiscal_ex_forecasts) # upload exogenous variables future values
data(us_fiscal_cond_forecasts) # upload a matrix with projected ttr
#' set.seed(123)
specification = specify_bsvar_t$new(us_fiscal_lsuw, exogenous = us_fiscal_ex)
#> The identification is set to the default option of lower-triangular structural matrix.
burn_in = estimate(specification, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> with t-distributed structural skocks |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior = estimate(burn_in, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> with t-distributed structural skocks |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# forecast 2 years ahead
predictive = forecast(
posterior,
horizon = 8,
exogenous_forecast = us_fiscal_ex_forecasts,
conditional_forecast = us_fiscal_cond_forecasts
)
summary(predictive)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 -8.914237 0 -8.914237 -8.914237
#> 2 -8.908921 0 -8.908921 -8.908921
#> 3 -8.903604 0 -8.903604 -8.903604
#> 4 -8.898288 0 -8.898288 -8.898288
#> 5 -8.892971 0 -8.892971 -8.892971
#> 6 -8.887655 0 -8.887655 -8.887655
#> 7 -8.882338 0 -8.882338 -8.882338
#> 8 -8.877022 0 -8.877022 -8.877022
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 -9.837755 0.1021440 -9.982446 -9.702030
#> 2 -9.811925 0.2141586 -10.059346 -9.493666
#> 3 -9.791147 0.2803300 -10.116954 -9.353528
#> 4 -9.747606 0.2946255 -10.171010 -9.385011
#> 5 -9.738103 0.3640586 -10.325447 -9.350938
#> 6 -9.720222 0.3460256 -10.288509 -9.364275
#> 7 -9.716970 0.3100670 -10.236397 -9.365467
#> 8 -9.782993 0.3657178 -10.464212 -9.498823
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 -7.063645 0.04726872 -7.139990 -7.002274
#> 2 -6.996275 0.09733393 -7.077062 -6.844588
#> 3 -6.984982 0.08654321 -7.057481 -6.829573
#> 4 -6.940121 0.11152654 -7.049536 -6.777686
#> 5 -6.929928 0.12604763 -7.073341 -6.759790
#> 6 -6.931170 0.12960130 -7.066684 -6.764238
#> 7 -6.969629 0.15906562 -7.172552 -6.736426
#> 8 -6.978067 0.17145520 -7.218679 -6.728235
#>
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar_t$new(exogenous = us_fiscal_ex) |>
estimate(S = 5) |>
estimate(S = 10) |>
forecast(
horizon = 8,
exogenous_forecast = us_fiscal_ex_forecasts,
conditional_forecast = us_fiscal_cond_forecasts
) |> plot()
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> with t-distributed structural skocks |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> with t-distributed structural skocks |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
