eer-robustness
GitHub为欧洲经济评论(EER)风格论文构建严谨的稳健性检验电池，涵盖规范、样本、测量及推断等维度。通过系统化压力测试验证核心估计的可靠性，确保结果在多种合理变更下依然成立，并指导结果的组织与报告。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eer-robustness -g -y
SKILL.md
Frontmatter
{
"name": "eer-robustness",
"description": "Use when a European Economic Review (EER) result must be shown to survive specification, sample, measurement, and inference changes — the robustness battery referees demand. Builds the stress tests and organizes them; it does not establish the core identification or write the prose."
}
Robustness & Sensitivity (eer-robustness)
When to trigger
- The headline estimate exists but its fragility has not been probed
- A referee (or co-author) suspects the result is driven by one sample/spec choice
- Inference assumptions (clustering, dependence, multiple testing) are unexamined
- A structural/quantitative result's sensitivity to parameters is not shown
The EER robustness bar
A general-interest result must be believable beyond the authors' favorite specification. EER referees — methods-aware under single-anonymized review — expect a disciplined battery, not a scattershot appendix: vary the things that could plausibly overturn the result, report them transparently, and say which (if any) move the estimate. The goal is a result that is robust where it matters and honest where it is fragile. Robustness is not infinite specification mining; choose tests with a reason.
The robustness battery (choose by design)
| Dimension | Test | Why it matters |
|---|---|---|
| Specification | add/drop controls; alternative functional form; FE structure | shows the estimate is not a control artifact |
| Sample | leave-one-out (unit/region/year); alternative windows; trimming outliers | shows no single observation drives it |
| Measurement | alternative outcome/treatment definitions; alternative data source | shows it is not a coding choice |
| Estimator | heterogeneity-robust DiD vs TWFE; alternative IV/RDD bandwidth | shows method-robustness |
| Inference | clustering level; wild-cluster bootstrap (few clusters); spatial/cross-sectional dependence; randomization inference | shows SEs are valid under real dependence |
| Multiple testing | Romano–Wolf / Bonferroni–Holm across families | guards against cherry-picked significance |
| Structural | parameter sensitivity; alternative calibration targets; grid/tuning | shows quantity is not a tuning artifact |
| Pre-trends | honest-DiD sensitivity (Rambachan–Roth); placebo timing | bounds violations of parallel trends |
How to organize it
- Pick the threats that could actually overturn the claim — tie each test to a specific objection.
- Lead with the most dangerous test, not the easiest one.
- Report a coefficient-stability table or specification curve so the reader sees the distribution of estimates.
- State the verdict honestly: "the estimate ranges X–Y across N specifications; it loses significance only when Z."
- Push the long tail to the Supplementary material, keep the load-bearing tests in-text.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. EER is a general economics field journal; the DiD/IV/RDD chain serves its applied lane.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Checklist
- Each robustness test is tied to a named objection (not decorative)
- Sample robustness: leave-one-out and alternative windows shown
- Inference robustness: clustering justified; few-cluster / dependence handled
- Estimator robustness: modern vs naive estimator agree (or the gap is explained)
- Multiple-testing correction where several outcomes are tested
- Structural: parameter/calibration sensitivity reported
- A coefficient-stability table or spec curve summarizes the distribution
- Fragilities stated honestly, not hidden
Anti-patterns
- A robustness appendix that only adds controls and never threatens the result
- Reporting 20 specs that all "confirm" the result while omitting the one that breaks it
- Clustering at a convenient level to shrink standard errors
- Specification mining presented as robustness (no rationale per test)
- Burying a fragility the referee will find anyway — better to disclose and bound it
- Significance stars substituting for a coefficient-stability view
Worked vignette (illustrative)
An IO paper finds a merger raised prices 4%. A weak appendix re-runs with more controls. An EER battery: leave-one-market-out (range 3.1–4.6%, illustrative), alternative price index, synthetic-control placebo on untreated markets, wild-cluster bootstrap (28 markets), and a Romano–Wolf correction across the three outcomes. Verdict stated plainly: "the price effect is 3.1–4.6% and significant in all but the trimmed-outlier sample, where it is 2.0% (s.e. 1.1)." The reader trusts the number because its fragility was mapped.
Output format
【Core claim under test】one sentence
【Threats probed】[spec / sample / measurement / estimator / inference / MHT / structural]
【Most dangerous test + result】[...]
【Estimate range across specs】X–Y (where it breaks: Z)
【Honest fragilities】[...]
【Next step】eer-tables-figures (present the battery) or eer-referee-strategy
Version History
- 1839142 Current 2026-07-05 13:12
