jae-methods — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited jae-methods (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
JAE's workhorse is large-sample empirical archival research grounded in economics — observational capital-markets and contracting data analyzed with econometric, identification-focused designs — alongside analytical economic modeling. The journal favors economic analyses of accounting problems (capital-markets information content, contracting, disclosure, agency/monitoring) in the Watts-Zimmerman positive-accounting tradition. It does not publish normative prescriptions, behavioral lab experiments, or design-science artifacts; design accordingly.
Because accounting choices and disclosures are endogenous, a bare panel regression rarely survives review. Match the design to the prediction:
| Setting / claim | Identification strategy |
|---|---|
| A regulation/standard changes for some firms | Difference-in-differences around the shock; staggered DiD |
| A continuous threshold (covenant, index, size cut) | Regression discontinuity |
| Endogenous regressor, valid instrument available | IV / 2SLS; defend exclusion restriction explicitly |
| Self-selection into disclosure/treatment | Heckman selection; propensity-score matching |
| Information event (earnings, 8-K, disclosure) | Short-window event study (CARs), market-reaction design |
| Pure mechanism / equilibrium claim | Analytical model with assumptions, propositions, proofs |
State the identifying assumption in words (parallel trends, exclusion restriction, continuity at the cutoff) and show how the design satisfies it. A natural experiment from a regulatory shock (SOX, Reg FD, IFRS/ASU adoption, an enforcement change) is the most persuasive JAE design when available.
If the contribution is the model: state primitives and the information structure, solve for equilibrium, present comparative statics as testable propositions, and put proofs in an appendix. Keep assumptions economically interpretable.
For the empirical / causal lane, estimate and audit rather than only specify. Full map: execution-with-mcp. JAE is empirical accounting with an economics lens; treat identification and weak-IV-robust inference as the binding constraints.
detect_design → recommend → fit with as_handle=true → audit_result toenumerate the checks the design owes.
callaway_santanna / sun_abraham + bacon_decomposition+ honest_did_from_result. IV: effective_f_test + anderson_rubin_ci. RDD: rdrobust + mccrary_test.
romano_wolf for the many-outcomefamily-wise correction reviewers expect.
Match the toolchain to the reviewer pool, and report the effect size the venue wants. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
【Design】DiD / RD / IV / matching / event study / analytical model
【Identifying assumption】parallel trends / exclusion / continuity ...
【Shock or instrument】...
【Sample waterfall】population → merges → exclusions → final N
【Key proxies & expected signs】...
【Threats to identification】... and how addressed
【Next step】jae-data-analysis~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.