Representative cumulative dynamic multipliers

Usage

representative_cdm(object, ...)

# Default S3 method
representative_cdm(object, ...)

# S3 method for class 'PosteriorCDM'
representative_cdm(
  object,
  method = c("median_target", "most_likely_admissible"),
  center = c("median", "mean"),
  variables = NULL,
  shocks = NULL,
  horizons = NULL,
  metric = c("l2", "weighted_l2"),
  standardize = c("none", "sd"),
  probability = 0.9,
  ...
)

# S3 method for class 'PosteriorBSVAR'
representative_cdm(
  object,
  horizon = NULL,
  method = c("median_target", "most_likely_admissible"),
  center = c("median", "mean"),
  variables = NULL,
  shocks = NULL,
  horizons = NULL,
  metric = c("l2", "weighted_l2"),
  standardize = c("none", "sd"),
  probability = 0.9,
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

Arguments

object: A posterior model object or a PosteriorIR.
...: Additional arguments passed to computation methods.
method: Representative-model selection method.
center: Posterior summary used as the target for median-target selection.
variables: Optional subset of response variables.
shocks: Optional subset of shocks.
horizons: Optional subset of horizons.
metric: Distance metric used for median-target selection.
standardize: Optional standardisation used in distance computation.
probability: Equal-tailed interval probability used for summaries.
horizon: Forecast horizon when object is a posterior model object.
scale_by: Optional scaling mode for CDMs.
scale_var: Optional scaling variable specification.

Value

A list of class RepresentativeCDM (inheriting from RepresentativeResponse) with elements representative_draw (the selected CDM array), posterior_draws (all CDM draws), draw_index (integer index of the selected draw), method, score, target_summary, selection_spec, probability, and object_type.

Examples

data(us_fiscal_lsuw, package = "bsvars")
spec <- bsvars::specify_bsvar$new(us_fiscal_lsuw, p = 1)
#> The identification is set to the default option of lower-triangular structural matrix.
post <- bsvars::estimate(spec, S = 5, show_progress = FALSE)

rep_cdm <- representative_cdm(post, horizon = 3)
rep_cdm$draw_index
#> [1] 4