jfm-robustness
Use when results may be sensitive to liquidity-measure choice, sample filters, microstructure noise, or inference for a Journal of Financial Markets (JFM) manuscript. Builds the design-based robustness ledger; it does not invent evidence or citations.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-robustness --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.
# Robustness Strategy (jfm-robustness) ## When to trigger - The headline holds with the chosen liquidity measure but you have not shown it survives alternatives - Results may flip with a different sample period, asset universe, or filter rule - Inference uses plain OLS standard errors on data that are autocorrelated and cross-correlated (panel of stocks over time) - Microstructure noise (bid-ask bounce, stale quotes, discreteness) could be generating the effect - A referee will ask "is this the mechanism or the measurement?" and you need each check mapped to a specific threat ## The JFM robustness ledger JFM referees do not want a wall of robustness tables; they want **each check tied to a named threat** to the microstructure interpretation. Build the ledger threat-first. | Threat to the microstructure claim | Robustness check that addresses it | |------------------------------------|-------------------------------------| | It's the measure, not the mechanism | Re-run with alternative liquidity/impact constructs (quoted↔effective↔realized; Amihud↔intraday impact) | | It's the filter / sample | Vary inclusion screens, period, asset universe, price/penny screens; subsample by cap/vol
- When to trigger
- The JFM robustness ledger
- Sequencing the checks
- The microstructure-noise battery
- Worked ledger (illustrative)
- Calibrating how much is enough
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Inference is the most-failed robustness dimension at JFM
- Anti-patterns
- Turning robustness into corroboration
- Output format
What does the jfm-robustness skill do?
Use when results may be sensitive to liquidity-measure choice, sample filters, microstructure noise, or inference for a Journal of Financial Markets (JFM) manuscript. Builds the design-based robustness ledger; it does not invent evidence or citations.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-robustness --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.