Highest Density Regions for koma_forecast Objects

Computes highest density regions (HDRs) for predictive draws in a koma_forecast object.

Usage

# S3 method for class 'koma_forecast'
hdr(
  x,
  variables = NULL,
  probs = c(0.5, 0.99),
  n_grid = 4096,
  integration = c("monte_carlo", "grid"),
  mc_use_observed = FALSE,
  mc_draws = NULL,
  mc_quantile_type = 7,
  bw = "nrd0",
  adjust = 1,
  kernel = "gaussian",
  ...
)

Arguments

x: A koma_forecast object.
variables: Optional character vector of endogenous variables to include. Defaults to all variables in x$forecasts.
probs: Numeric vector of target mass levels. Values in $(0, 1]$ or $[0, 100]$ are accepted. Default is c(0.5, 0.99).
n_grid: Number of grid points for the KDE. Default is 4096.
integration: Integration approach for the HDR cutoff. Use "grid" for grid-based integration or "monte_carlo" for Monte Carlo integration. Default is "monte_carlo".
mc_use_observed: Logical. If TRUE, Monte Carlo integration uses the observed draws. This can be a reasonable approximation for large x. If FALSE, draws are sampled from the KDE mixture; only implemented for kernel = "gaussian".
mc_draws: Optional integer number of draws for Monte Carlo sampling when mc_use_observed = FALSE. Defaults to max(length(x), 2000).
mc_quantile_type: Quantile type passed to stats::quantile for the Monte Carlo cutoff. Type 1 matches the order-statistic construction in Hyndman (1996), while type 7 (the default) interpolates between adjacent order statistics for a smoother, lower-variance cutoff that is asymptotically equivalent. Default is 7.
bw: Bandwidth for density. Default is "nrd0".
adjust: Bandwidth adjustment factor for density. Default is 1.
kernel: Kernel for density. Default is "gaussian".
...: Additional arguments forwarded to density. Do not pass n, bw, adjust, or kernel here.

Value

A list with class c("koma_forecast_hdr", "koma_hdr") containing HDR intervals per variable and horizon. The object also includes per-horizon modes, cutoff levels, and achieved masses.