Implicit Forecasts plot

Usage

implicit_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,
  ...
)

ggimplicit_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: "tc_estimates" object.
xlim, ylim: x and y limits of the plot. If xlim is defined and not ylim, then ylim is determined automatically.
col_sa, col_tc: color of the seasonally adjusted and trend-cycle components.
col_i_f: color of the implicit forecasts.
xlab, ylab: x and y axis labels.
lty_last_tc, lty_i_f: line type of the last values of the trend-cycle component and for the implicit forecasts.
n_last_tc: number of last values of the trend-cycle component to be plotted with a different line type (to emphasize that there is higher variability for the last estimates). If NULL, then n_last_tc is equal to the MCD statistic.
...: other parameters.
legend_tc, legend_sa, legend_i_f: legend of the trend-cycle and seasonally adjusted components and for implicit forecasts.

Examples

tc_mod <- henderson_smoothing(french_ipi[, "manufacturing"])
implicit_forecasts_plot(tc_mod, xlim = c(2022, 2025))