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R6 Class Representing Forecasts

Source: R/forecast.R
specify_forecasts.Rd

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

Public methods

Method new()

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

Usage

specify_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.

Method get_forecasts()

Converts the Forecasts object to a list.

Usage

specify_forecasts$get_forecasts()

Returns

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

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_forecasts$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

spec = specify_bsvarSIGN$new(optimism * 100)
post = estimate(spec, 5)
#> **************************************************|
#>  bsvarSIGNs: Bayesian Structural VAR with sign,   |
#>              zero and narrative restrictions      |
#> **************************************************|
#>  Progress of simulation for 5 independent draws
#>  Press Esc to interrupt the computations
#> **************************************************|
fore = forecast(post, 4)
apply(fore$forecasts, 1:2, mean) # compute mean forecasts 
#>               [,1]          [,2]          [,3]          [,4]
#> [1,]    84.5451248    85.1549035    84.8759131  8.445742e+01
#> [2,] -1070.4948614 -1069.6004138 -1070.8006405 -1.069942e+03
#> [3,]  -338.6833530  -338.5817412  -338.7693864 -3.387349e+02
#> [4,]     0.5422752     0.5167357     0.1908743  1.367001e-02
#> [5,]  -784.2297911  -784.9333610  -785.2907366 -7.851993e+02