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jfe-robustness

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Use when a Journal of Financial Economics (JFE) result needs the exhaustive robustness battery the journal is known for — alternative measures, subsamples, specifications, and falsification. Builds the stress-test plan; it does not choose the core estimator (jfe-empirical-design) or the identification design (jfe-identification).

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-Financial-Economics-Skills/skills/jfe-robustness/SKILL.md

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Robustness Battery (jfe-robustness)

When to trigger

  • The result holds in the baseline but you have not tried obvious alternatives
  • A referee could ask "does this survive a different measure / sample / specification?"
  • You are unsure which robustness tests belong in the main text vs. the Internet Appendix
  • You suspect a result might be fragile and want to find out before a referee does

Why robustness is JFE's signature

JFE's referee culture rewards thoroughness: a result is credible only after it survives a wide battery of sensible alternatives, and reviewers expect every alternative explanation to be addressed, not merely the convenient ones. A fragile result that breaks under a routine variation is a classic JFE rejection. The headline tests live in the main text; the rest go to the Internet Appendix, which JFE asks you to append to the end of the main manuscript file (see jfe-internet-appendix).

Because JFE mandates a code + non-proprietary-data deposit (Mendeley Data) at acceptance for post-2021 submissions, your robustness battery must be runnable, not just described — referees increasingly probe reproducibility after the field-wide concerns about non-replicable factors (the Fama-French factor lineage is the benchmark) and fragile staggered-DID results.

The robustness dimensions

Work through each; for each, decide main-text vs. appendix.

1. Alternative measures

  • Re-measure the dependent and key independent variables (different proxies, data sources, definitions).
  • Show the result is not an artifact of one operationalization.

2. Alternative specifications

  • Add/remove fixed effects; vary the control set; show coefficient stability (a coefficient-stability / Oster bounds argument for omitted-variable bias where relevant).
  • Functional form: levels vs. logs, linear vs. nonparametric.

3. Alternative samples

  • Drop influential years (e.g., the financial crisis), industries, or large firms.
  • Subperiod stability; exclude the most/least-treated units.

4. Alternative inference

  • Re-cluster at a different level; wild-cluster bootstrap when clusters are few.
  • Permutation/randomization inference where appropriate.

5. Falsification & placebos

  • Outcomes that should not respond if your story is right.
  • Placebo timing or placebo treatment groups.

6. Ruling out alternative explanations

  • For each rival story a referee could tell, design a test that distinguishes it from yours.
  • This is the single most important block for JFE — list the alternatives explicitly and kill them one by one.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JFE is finance top-3 (with JF, RFS) — corporate-causal chain for corporate papers, factor-zoo haircut for asset pricing; attribute canon to the correct top-3 journal.

  • 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

  • Key variables re-measured with at least one alternative proxy
  • Specification varied (FE set, controls, functional form) with stable coefficients
  • Subsample / subperiod stability shown; crisis or outlier periods isolated
  • Inference re-done at an alternative cluster level (or wild bootstrap)
  • Falsification / placebo tests run and reported
  • Each named alternative explanation has a distinguishing test
  • Main-text vs. Internet-Appendix split decided for every test

Anti-patterns

  • Reporting only the robustness checks that pass ("cherry-picked specifications")
  • A "robustness" section that re-runs the same model with one extra control and calls it done
  • Leaving an obvious rival explanation untested and hoping no referee notices
  • A result that flips sign or loses significance under a routine variation, reported without comment
  • Dumping every check into the main text so the paper becomes unreadable

Output format

【Headline result】...
【Robustness done】measures[...] specs[...] samples[...] inference[...] placebos[...]
【Alternatives ruled out】[story -> test] pairs
【Fragile under】[...] (if any — fix before submission)
【Main-text vs. appendix】split decided: yes/no
【Next】jfe-tables-figures