From aej-applied-economics-skills
Builds robustness suites for AEJ: Applied manuscripts to show headline estimates survive specification, sample, and inference choices.
How this skill is triggered — by the user, by Claude, or both
Slash command
/aej-applied-economics-skills:aeja-robustnessThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- The main estimate is in hand and you need to show it is not an artifact of one specification
AEJ: Applied referees probe whether the headline number is stable, honestly inferred, and not the product of researcher degrees of freedom. Robustness here is not a wall of regressions — it is a targeted set of checks each tied to a specific threat to the design. Map every plausible objection to the one check that answers it, and report the checks so the reader sees the estimate barely moves.
| Threat to the result | The check that answers it |
|---|---|
| Omitted confounders | Oster δ / coefficient-stability bounds; added controls in steps |
| Specification search | a specification curve / multiverse; pre-registered primary spec |
| Functional form | levels vs logs, alternative outcome definitions, nonparametric version |
| Sample selection | drop influential units, alternative inclusion rules, balanced vs unbalanced panel |
| Inference too narrow | clustered SEs at the right level, wild-cluster bootstrap (few clusters), randomization inference |
| Design-specific fragility | DID: honest-DID bounds; RD: bandwidth/donut; IV: weak-IV-robust set |
| Multiple outcomes/subgroups | Romano–Wolf / List–Shaikh–Wooldridge MHT adjustment |
An IV estimate of the return to a training program is 0.11 (s.e. 0.04). The robustness suite: (i) effective F of 23 rules out weak instruments; (ii) the Anderson–Rubin 95% set is [0.04, 0.19], so inference is not weak-IV-fragile; (iii) Oster δ implies selection on unobservables would need to be 1.8× selection on observables to nullify it; (iv) wild-cluster bootstrap with 14 clusters keeps the CI away from zero; (v) dropping the largest region moves the estimate to 0.10. The point estimate barely moves — the AEJ: Applied target.
【Primary spec】declared / pre-registered? [Y/N] — estimate: ___ (s.e. ___)
【Threat → check map】selection: ___ | spec-search: ___ | form: ___ | sample: ___ | inference: ___ | design: ___
【Inference】clustering level: ___; few-cluster/randomization: ___
【Design sensitivity】honest-DID / RD bandwidth / weak-IV set: ___
【Estimate stability】range across checks: [___, ___]; checks that move it: ___
【Next step】aeja-tables-figures
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin aej-applied-economics-skillsBuilds a robustness suite for REStat manuscripts: tests whether headline estimates survive specification, sample, measurement, identification, and inference alternatives.
Organizes robustness checks for JEEA manuscripts around threats a general-interest referee would raise, ensuring headline results are stable to specification, sample, inference, and assumption perturbations.
Organizes robustness checks for IER papers by threat to load-bearing assumption, without running regressions. Helps structure responses to referee concerns.