eer-robustness

Testing & Quality

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.

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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

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