R6 Class Representing Forecasts — specify

R6 class representing draws from the predictive density of a Bayesian Structural Vector Autoregression model.

Value

An object of class Forecasts.

Details

The class contains the following objects:

forecasts: An N x horizon x S array containing draws from the predictive density.
forecast_mean: An N x horizon x S array containing the conditional means of the predictive density.
forecast_covariance: An N x N x horizon x S array containing the conditional covariance matrices of the predictive density.
Y: An N x T matrix containing the data on the dependent variables used for estimation.

The method as_list() returns the contents of the Forecasts object as a list.

Public fields

forecasts: An N x horizon x S numeric array containing draws from the predictive density.
forecast_mean: An N x horizon x S numeric array containing the conditional means of the predictive density.
forecast_covariance: An N x N x horizon x S numeric array containing the conditional covariance matrices of the predictive density.
Y: An N x T numeric matrix containing the data on the dependent variables used for estimation.

Methods

`Forecasts$new()`

Creates a new Forecasts object from the output of the forecasting procedure.

Usage

Forecasts$new(output, Y)

Arguments

output: A list containing the forecasting output, including forecasts, forecast_mean, and forecast_cov.
Y: An N x T matrix containing the data on the dependent variables.

Returns

An object of class Forecasts.

`Forecasts$get_forecasts()`

Converts the Forecasts object to a list.

Usage

Forecasts$get_forecasts()

Returns

A list containing forecasts, forecast_mean, forecast_covariance, and Y.

`Forecasts$clone()`

The objects of this class are cloneable with this method.

Usage

Forecasts$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

spec = specify_bsvar$new(us_fiscal_lsuw)
#> The identification is set to the default option of lower-triangular structural matrix.
burn = estimate(spec, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR model                 |
#> **************************************************|
#>  Progress of the MCMC simulation for 5 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
post = estimate(burn, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR model                 |
#> **************************************************|
#>  Progress of the MCMC simulation for 5 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
fore = forecast(post, 4)
apply(fore$forecasts, 1:2, mean) # compute mean forecasts 
#>           [,1]      [,2]      [,3]      [,4]
#> [1,] -8.888228 -8.932025 -8.956138 -8.960784
#> [2,] -9.705272 -9.521921 -9.485589 -9.430052
#> [3,] -7.081218 -7.174442 -7.199131 -7.224809