| 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) or benjamini_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 exact
suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix.
JF execution walkthrough.
Checklist
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
