jae-methods
Use when designing the empirical (or analytical) approach for a Journal of Accounting and Economics (JAE) manuscript — choosing an identification strategy (natural experiments, DiD, IV, RD) for large-sample archival capital-markets/contracting/disclosure data, or structuring an economic model, so the design can credibly support the economic prediction.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jae-methods --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Research Design & Identification for JAE (jae-methods) ## When to trigger - You have a prediction but no credible way to rule out endogeneity or reverse causality - A reviewer will ask "is this causal or just correlation?" - You must choose between an archival quasi-experiment and an analytical model - Your treatment (a disclosure, a standard, a contract feature) is plausibly chosen, not random ## JAE's dominant methodology 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. ## Design for identification Because accounting choices and disclosures are endogenous, a bare panel regression rarely survives review. Match the design to the prediction: | Setting / claim | Identificatio
- When to trigger
- JAE's dominant methodology
- Design for identification
- Sample and measurement design
- Analytical-model design
- Referee pre-mortem
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the jae-methods skill do?
Use when designing the empirical (or analytical) approach for a Journal of Accounting and Economics (JAE) manuscript — choosing an identification strategy (natural experiments, DiD, IV, RD) for large-sample archival capital-markets/contracting/disclosure data, or structuring an economic model, so the design can credibly support the economic prediction.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jae-methods --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.