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Plots fitted values of dependent variables

Source: R/plot.R
plot.ForecastsPANEL.Rd

Plots of fitted values of dependent variables including their median and percentiles.

Usage

# S3 method for class 'ForecastsPANEL'
plot(
  x,
  which_c,
  probability = 0.9,
  data_in_plot = 1,
  col = "#1614B1",
  main,
  xlab,
  mar.multi = c(1, 4.6, 0, 2.1),
  oma.multi = c(6, 0, 5, 0),
  ...
)

Arguments

x

an object of class ForecastsPANEL obtained using the forecast() function containing posterior draws of fitted values of dependent variables.

which_c

a positive integer or a character string specifying the country for which the forecast should be plotted.

probability

a parameter determining the interval to be plotted. The interval stretches from the 0.5 * (1 - probability) to 1 - 0.5 * (1 - probability) percentile of the posterior distribution.

data_in_plot

a fraction value in the range (0, 1) determining how many of the last observations in the data should be plotted with the forecasts.

col

a colour of the plot line and the ribbon

main

an alternative main title for the plot

xlab

an alternative x-axis label for the plot

mar.multi

the default mar argument setting in graphics::par. Modify with care!

oma.multi

the default oma argument setting in graphics::par. Modify with care!

...

additional arguments affecting the summary produced.

See also

forecast.PosteriorBVARPANEL

Author

Tomasz Woźniak wozniak.tom@pm.me

Examples

specification = specify_bvarPANEL$new(ilo_dynamic_panel)  # specify the model
burn_in       = estimate(specification, 10)               # run the burn-in
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#>             Panel Vector Autoregressions          |
#> **************************************************|
#>  Progress of the MCMC simulation for 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
posterior     = estimate(burn_in, 10)                     # estimate the model
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#>             Panel Vector Autoregressions          |
#> **************************************************|
#>  Progress of the MCMC simulation for 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|

# forecast 6 years ahead
predictive    = forecast(posterior, 6, conditional_forecast = ilo_conditional_forecasts)
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#>             Panel Vector Autoregressions          |
#> **************************************************|
#>  Progress of sampling 10 draws from
#>     the predictive density for 189 countries
#>     Press Esc to interrupt the computations
#> **************************************************|
plot(predictive, which_c = "POL")                                # plot forecasts


# workflow with the pipe |>
############################################################
set.seed(123)
ilo_dynamic_panel |>
  specify_bvarPANEL$new() |>
  estimate(S = 10) |> 
  estimate(S = 10) |> 
  forecast(horizon = 6, conditional_forecast = ilo_conditional_forecasts) |>
  plot(which_c = 135)
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#>             Panel Vector Autoregressions          |
#> **************************************************|
#>  Progress of the MCMC simulation for 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#>             Panel Vector Autoregressions          |
#> **************************************************|
#>  Progress of the MCMC simulation for 10 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#>             Panel Vector Autoregressions          |
#> **************************************************|
#>  Progress of sampling 10 draws from
#>     the predictive density for 189 countries
#>     Press Esc to interrupt the computations
#> **************************************************|