BVAR (version 1.0.5)

plot.bvar_fcast: Plotting method for Bayesian VAR predictions

Description

Plotting method for forecasts obtained from predict.bvar. Forecasts of all or a subset of the available variables can be plotted.

Usage

# S3 method for bvar_fcast
plot(
  x,
  vars = NULL,
  col = "#737373",
  t_back = 1,
  area = FALSE,
  fill = "#808080",
  variables = NULL,
  orientation = c("vertical", "horizontal"),
  mar = c(2, 2, 2, 0.5),
  ...
)

Value

Returns x invisibly.

Arguments

x

A bvar_fcast object, obtained from predict.bvar.

vars

Optional numeric or character vector. Used to subset the plot to certain variables by position or name (must be available). Defaults to NULL, i.e. all variables.

col

Character vector. Colour(s) of the lines delineating credible intervals. Single values will be recycled if necessary. Recycled HEX color codes are varied in transparency if not provided (e.g. "#737373FF"). Lines can be bypassed by setting this to "transparent".

t_back

Integer scalar. Number of observed datapoints to plot ahead of the forecast.

area

Logical scalar. Whether to fill the credible intervals using polygon.

fill

Character vector. Colour(s) to fill the credible intervals with. See col for more information.

variables

Optional character vector. Names of all variables in the object. Used to subset and title. Taken from x$variables if available.

orientation

String indicating the orientation of the plots. Defaults to "v" (i.e. vertical); may be set to "h" (i.e. horizontal).

mar

Numeric vector. Margins for par.

...

Other graphical parameters for par.

See Also

bvar; predict.bvar

Examples

# \donttest{
# Access a subset of the fred_qd dataset
data <- fred_qd[, c("CPIAUCSL", "UNRATE", "FEDFUNDS")]
# Transform it to be stationary
data <- fred_transform(data, codes = c(5, 5, 1), lag = 4)

# Estimate a BVAR using one lag, default settings and very few draws
x <- bvar(data, lags = 1, n_draw = 1000L, n_burn = 200L, verbose = FALSE)

# Store predictions ex-post
predict(x) <- predict(x)

# Plot forecasts for all available variables
plot(predict(x))

# Subset to variables in positions 1 and 3 via their name
plot(predict(x), vars = c("CPI", "FED"))

# Subset via position, increase the plotted forecast horizon and past data
plot(predict(x, horizon = 20), vars = c(1, 3), t_back = 10)

# Adjust confidence bands and the plot's orientation
plot(predict(x, conf_bands = 0.25), orientation = "h")

# Draw areas inbetween the confidence bands and skip drawing lines
plot(predict(x), col = "transparent", area = TRUE)

# Plot a conditional forecast (with a constrained second variable).
plot(predict(x, cond_path = c(1, 1, 1, 1, 1, 1), cond_var = 2))
# }