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Plot Underlying Forecasts for Trend-Cycle Estimates

Source: R/plot_underlying_forecasts.R
underlying_forecasts_plot.Rd

Base R (underlying_forecasts_plot()) and ggplot2 (ggunderlying_forecasts_plot()) graphics for plotting trend-cycle estimates alongside their underlying forecasts.

Usage

underlying_forecasts_plot(
  object,
  xlim = NULL,
  ylim = NULL,
  col_tc = "#E69F00",
  col_sa = "black",
  col_i_f = col_sa,
  xlab = "",
  ylab = "",
  lty_last_tc = 2,
  lty_i_f = 3,
  n_last_tc = 4,
  ...
)

ggunderlying_forecasts_plot(
  object,
  xlim = NULL,
  ylim = NULL,
  col_tc = "#E69F00",
  col_sa = "black",
  col_i_f = col_sa,
  lty_last_tc = 2,
  lty_i_f = 3,
  n_last_tc = 4,
  legend_tc = "Trend-cycle",
  legend_sa = "Seasonally adjusted",
  legend_i_f = "Implicit forecasts",
  ...
)

Arguments

object

An object of class "tc_estimates".

xlim, ylim

Limits for the x- and y-axes. If xlim is specified and ylim is NULL, ylim is determined automatically based on the truncated series.

col_sa, col_tc

Colors used for the seasonally adjusted and trend-cycle components, respectively.

col_i_f

Color of the forecasts lines.

xlab, ylab

Character strings for the x- and y-axis labels.

lty_last_tc, lty_i_f

Line types used for the last values of the trend-cycle component and for the forecasts, respectively.

n_last_tc

Number of final values of the trend-cycle component to plot with a distinct line type (lty_last_tc), emphasizing higher uncertainty in recent estimates. If NULL (the default), n_last_tc is set to the Month of Cyclical Dominance (MCD) statistic.

...

Additional graphical parameters passed to internal plotting functions.

legend_tc, legend_sa, legend_i_f

Character strings specifying the legend labels for the trend-cycle, the seasonally adjusted series, and the forecasts, respectively.

Value

  • underlying_forecasts_plot() returns NULL invisibly and is called for its side effect (drawing a plot).

  • ggunderlying_forecasts_plot() returns a ggplot object.

Details

underlying_forecasts_plot() produces a base R graphic, whereas ggunderlying_forecasts_plot() creates a ggplot2 object that can be further customized.

Examples

if (FALSE) { # rjd3jars::check_java_version(silent = TRUE)
tc_mod <- henderson_smoothing(french_ipi[, "manufacturing"])
underlying_forecasts_plot(tc_mod, xlim = c(2022, 2025))
}