forecast.PosteriorBSVARMIX {bsvars}

Forecasting using Structural Vector Autoregression

Description

Samples from the joint predictive density of all of the dependent variables at forecast horizons from 1 to horizon specified as an argument of the function.

Usage

## S3 method for class 'PosteriorBSVARMIX'
forecast(posterior, horizon, exogenous_forecast = NULL)

Arguments

posterior

posterior estimation outcome - an object of class PosteriorBSVARMIX 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.

Value

A list of class Forecasts containing the draws from the predictive density and for heteroskedastic models the draws from the predictive density of structural shocks conditional standard deviations. The output elements include:

forecasts

an NxTxS array with the draws from predictive density

forecasts_sigma

provided only for heteroskedastic models, an NxTxS array with the draws from the predictive density of structural shocks conditional standard deviations

Author(s)

Tomasz Woźniak [e-mail]

Examples

# upload data
data(us_fiscal_lsuw)

# specify the model and set seed
set.seed(123)
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, p = 1, M = 2)

# run the burn-in
burn_in        = estimate(specification, 10)

# estimate the model
posterior      = estimate(burn_in, 20)

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

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_mix$new(p = 1, M = 2) |>
  estimate(S = 10) |>
  estimate(S = 20) |>  
  forecast(horizon = 4) -> predictive