Given a design, runs the estimation and the route-appropriate battery of diagnostics in one call, dispatching on `design$exogenous`:
**share** (Goldsmith-Pinkham, Sorkin & Swift 2020): Rotemberg-weight decomposition, leave-one-out sensitivity, and — if `covariates` are supplied — a share-balance check; a pre-trend check if `pre_y` is supplied.
**shift** (Borusyak, Hull & Jaravel 2022): effective-shock / exposure-concentration summary, leave-one-out sensitivity, and the shock-balance hook.
Estimation always reports the full SE panel (naive / EHW / cluster / AKM / AKM0). The point estimate and first-stage F are common to both routes.
Usage
ssb_pipeline(
design,
covariates = NULL,
pre_y = NULL,
placebo_y = NULL,
shock_covariates = NULL,
top = 5,
level = 0.95
)Arguments
- design
An [ssb_design()] object.
- covariates
Optional observables for the share-balance check (share route).
- pre_y
Optional pre-period outcome for [ssb_pretrend()].
- placebo_y
Optional placebo outcome for [ssb_placebo()].
- shock_covariates
Optional shock-level characteristics (a data.frame keyed by sector) for [ssb_shock_balance()] on the shift route.
- top
Number of top-weight sectors for the sensitivity diagnostics.
- level
Confidence level.