Decomposes the shift-share 2SLS estimate into a weighted sum of the just-identified estimates that use each sector's share as a single instrument, following Goldsmith-Pinkham, Sorkin and Swift (2020): $$\hat\beta = \sum_n \hat\alpha_n \hat\beta_n,\qquad \hat\alpha_n = \frac{g_n\, \tilde s_n' \tilde x}{\sum_{n'} g_{n'}\, \tilde s_{n'}' \tilde x},$$ where tildes denote residualisation on the controls (and, in panels, sector-cells are sector \(\times\) period pairs). The weights \(\hat\alpha_n\) sum to one and measure the sensitivity of \(\hat\beta\) to misspecification of each sector's instrument; a small number of large weights is a warning sign. Unlike Goodman-Bacon weights, negative Rotemberg weights are not automatically problematic.
Value
A `data.frame` of class `ssb_rotemberg`, one row per sector-cell, with columns `sector`, `g` (shock), `alpha` (Rotemberg weight), `beta` (just-identified estimate), `F` (first-stage F of that instrument), and `sign`. Carries the overall estimate `beta_hat` as an attribute. Pass it to [ssb_plot_rotemberg()] for the canonical figure.