From aej-macroeconomics-skills
Builds a macro-robustness program for AEJ: Macro manuscripts, testing headline results across specification, sample, identification, and tuning choices.
How this skill is triggered — by the user, by Claude, or both
Slash command
/aej-macroeconomics-skills:aejmac-robustnessThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- The headline number rests on one specification, one sample, one lag length, or one grid
Macro inference is fragile in characteristic ways: short effective samples, structural breaks (Great Moderation, ZLB, COVID), specification forks (lag length, detrending, prior, calibration target), and method dependence (SVAR vs. LP; perturbation vs. global). The AEJ: Macro robustness bar is to show the headline quantity survives the choices a skeptical macro referee would flip, and to be honest where it does not. Robustness is not a graveyard of extra tables — it is a targeted defense of the specific number the paper claims.
A paper reports a fiscal multiplier of 1.2 from a proxy-VAR on 1960–2019. A referee suspects it is driven by the volatile pre-1984 period. The robustness program: re-estimate on 1984–2019, exclude the ZLB years, and corroborate with local projections using the same narrative instrument. Suppose the multiplier is 1.2 full sample, 1.0 post-1984, 1.4 at the ZLB, all with overlapping bands, and the LP cross-check agrees within 0.1 — the paper then claims a multiplier "around 1.0–1.4 depending on the monetary regime," which is more credible and more interesting than the single number (illustrative).
【Headline quantity defended】... (baseline value)
【Empirical robustness】sample splits / specs / method cross-check / inference variants
【Quantitative robustness】alt targets / parameters / solution accuracy
【Placebo + external validity】...
【Where it weakens (honest)】...
【Next step】aejmac-tables-figures
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin aej-macroeconomics-skillsBuilds robustness suites for AEJ: Applied manuscripts to show headline estimates survive specification, sample, and inference choices.
Builds a robustness suite for REStat manuscripts: tests whether headline estimates survive specification, sample, measurement, identification, and inference alternatives.
Organizes robustness checks for IER papers by threat to load-bearing assumption, without running regressions. Helps structure responses to referee concerns.