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Recomputes the overall estimate dropping each of the top sectors (by |Rotemberg weight|) one at a time, to see whether identification hinges on a single shock.

Usage

ssb_loo(
  design,
  top = 5,
  se = c("none", "iid", "ehw", "cluster", "akm", "akm0"),
  level = 0.95
)

Arguments

design

An [ssb_design()] object.

top

Number of top-weight sectors to leave out in turn.

se

Standard-error method for a confidence interval on each leave-one-out estimate: `"none"` (default; point estimates only, the original behaviour) or one of `"iid"`, `"ehw"`, `"cluster"`, `"akm"`, `"akm0"` (each re-estimated on the reduced design via [ssb_estimate()]). With a CI you can read whether the estimate still excludes 0 after dropping the most influential shock; [ssb_plot_loo()] then draws the intervals.

level

Confidence level for the interval when `se` is not `"none"`.

Value

A `data.frame` with the dropped `sector`, its `alpha`, and the `beta_drop` obtained without it (plus the full-sample `beta_hat` attribute). When `se` is not `"none"` it also has `conf.low`/`conf.high` columns and `se_method`/`level` attributes.