Rotemberg-weight summary and correlations (GPSS diagnostic table)

Summarises the Rotemberg-weight diagnostic in the spirit of Goldsmith-Pinkham, Sorkin & Swift (2020): the top-weight share instruments, the largest single weight, the correlation of the weights with the just-identified estimates and first-stage F, and — if `covariates` are supplied — the correlation between each share instrument's Rotemberg weight and its exposure-weighted average of unit observables (do the high-weight share instruments load on systematically different places?).

Usage

ssb_weight_summary(design, covariates = NULL, top = 5)

Arguments

design: An [ssb_design()] object.
covariates: Optional unit-level observable columns in `data`.
top: Number of top-weight share instruments to display.

Value

A list (class `ssb_weight_summary`).

Examples

sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_weight_summary(d, covariates = "w1")
#> <ssBartik Rotemberg-weight summary>
#>   largest weight: alpha = 0.585 (6)
#>   cor(alpha, beta_k) = -0.21   cor(alpha, F) = 0.97
#>   cor(alpha, exposure-weighted covariate):
#>     w1                0.20
#>   top share instruments by |alpha|:
#>  sector  alpha  beta     F      g
#>       6 0.5850 1.486 12.24 -3.042
#>       3 0.1736 1.472  5.53  1.396
#>       8 0.1109 0.631  1.54  1.374
#>       4 0.0875 1.956  2.83 -0.669
#>       9 0.0165 2.041  1.22  0.233