Plots the median and an interval between two specified percentiles for a sequence of K random variables

Plots the median and an interval between two specified percentiles for a sequence of K random variables based on the S posterior draws provided for each of them.

Usage

plot_ribbon(
  draws,
  probability = 0.9,
  col = "#ff69b4",
  ylim,
  ylab,
  xlab,
  start_at = 0,
  add = FALSE,
  ...
)

Arguments

draws: a K x S matrix with S posterior draws of K random variables, or a K x S x N array with N such matrices
probability: a number from interval (0,1) denoting the probability content of the plotted interval. The interval stretches from the 0.5 * (1 - probability) to 1 - 0.5 * (1 - probability) percentile of the posterior distribution.
col: a colour of the plot
ylim: the range of the y axis
ylab: the label of the y axis
xlab: the label of the x axis
start_at: an integer to denote the beginning of the x axis range
add: a logical value. If TRUE the current ribbon plot is added to an existing plot
...: other graphical parameters to be passed to base::plot

Author

Tomasz Woźniak wozniak.tom@pm.me

Examples

data(us_fiscal_lsuw)                                               # upload data
set.seed(123)                                                      # set seed
specification  = specify_bsvar$new(us_fiscal_lsuw)                 # specify model
#> The identification is set to the default option of lower-triangular structural matrix.

burn_in        = estimate(specification, 10)                       # run the burn-in
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR model                 |
#> **************************************************|
#>  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, 20, thin = 1)                   # estimate the model
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR model                 |
#> **************************************************|
#>  Progress of the MCMC simulation for 20 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|
irf            = compute_impulse_responses(posterior, horizon = 4) # impulse responses
plot_ribbon(irf[1,1,,])