Fan chart

Visualization of (out-of-sample) predictive distribution.

Usage

# S3 method for class 'bayesianVARs_predict'
plot(
  x,
  dates = NULL,
  vars = "all",
  ahead = NULL,
  quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  n_col = 1L,
  first_obs = 1L,
  ...
)

Arguments

x

An object of type bayesianVARs_predict obtained via predict.bayesianVARs_bvar().

dates

optional vector of dates for labeling the x-axis. The default values is NULL; in this case, the axis will be labeled with numbers.

vars

character vector containing the names of the variables to be visualized. The default is "all" indicating that all variables are visualized.

ahead

Integer vector (or coercible to such) indicating which step ahead to plot. max(ahead) must be smaller equal to dim(x$predictions)[1].

quantiles

numeric vector indicating which quantiles to plot.

n_col

integer indicating the number of columns to use for plotting.

first_obs

integer indicating the first observation to be used for plotting.

...

Currently ignored!

Value

Returns x invisibly!

See also

Other plotting plot.bayesianVARs_bvar(), plot.bayesianVARs_fitted(), pairs.bayesianVARs_predict() posterior_heatmap().

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate from posterior predictive
predictions <- predict(mod, ahead = 1:3)
#> 'stable=TRUE': Calling 'stable_bvar()' to discard those posterior
#>           draws, that do not fulfill the stable criterion.
#> 
#>  385 stable posterior draws remaining for prediction!

# Visualize
plot(predictions, vars = 1:3, ahead = 1:3)