jf-empirical-design
Use when designing or stress-testing an asset-pricing test for a The Journal of Finance (JF) manuscript — factor models, Fama–MacBeth vs. panel, standard-error corrections, out-of-sample discipline. For corporate causal claims use jf-identification.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jf-empirical-design --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.
# Asset-Pricing Test Design (jf-empirical-design) ## When to trigger - You have a candidate predictor / anomaly / factor and must decide how to test it - You are unsure whether to run Fama–MacBeth, time-series factor regressions, or a panel - You report t-stats but have not addressed the standard-error subtleties of cross-sectional asset pricing - A referee will ask "is this data mining / does it survive multiple testing / does it work out of sample?" > Scope: this skill is for **asset-pricing tests**. For corporate/empirical causal effects, route to `jf-identification`. ## Choosing the test | Goal | Workhorse design | |-------------------------------------------------|-------------------------------------------------------------| | Does characteristic X price the cross-section? | Fama–MacBeth cross-sectional regressions + portfolio sorts | | Is a candidate factor priced / spanned? | Time-series regressions; GRS test; spanning vs. established factors | | Compare competing factor models | Alphas of test assets; max-Sharpe / HJ distance; model comparison | | Does a signal predict returns? | Predictive regressions + long-short; in/out-of-sample R² (Campbell–Thompson) | | Panel with fi
- When to trigger
- Choosing the test
- JF-specific standards
- Worked vignette — a risk-vs-mispricing horse race
- Referee-pushback patterns and the JF-specific fix
- Calibration anchors for JF asset pricing
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the jf-empirical-design skill do?
Use when designing or stress-testing an asset-pricing test for a The Journal of Finance (JF) manuscript — factor models, Fama–MacBeth vs. panel, standard-error corrections, out-of-sample discipline. For corporate causal claims use jf-identification.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jf-empirical-design --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.