jfqa-data-analysis
Use when running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES), winsorizing, fixed effects, clustered and Newey-West standard errors, robustness, and heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code. For theory papers, lighten this and document numerical examples instead.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfqa-data-analysis --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.
# JFQA Data Analysis (jfqa-data-analysis) Use this skill to execute and document the estimation for a **JFQA** empirical finance paper so it is both credible and reproducible from the code you will archive (see jfqa-replication-and-data-policy). ## Data construction (finance-specific) - Build from standard sources (CRSP, Compustat, CRSP/Compustat Merged, TAQ, IBES, TRACE, OptionMetrics) and document every filter (share codes, exchanges, financials/utilities exclusions, delisting returns). - **Winsorize or trim** outliers and disclose the cutoffs; finance variables (ratios, returns) have heavy tails. - Report the sample period, the number of firms and observations, and the unit of analysis. ## Estimation & inference - Use fixed effects appropriate to the question; justify the **clustering** dimension (firm, time, or two-way) — finance referees will ask. - For asset-pricing tests, use **Fama-MacBeth** with Newey-West or the appropriate correction; for panels, cluster-robust SEs. - Report **economic magnitudes** (e.g., effect of a one-SD change, basis points, alpha per month), not just significance stars. ## Robustness & heterogeneity - Alternative samples, alternative variable defini
- Data construction (finance-specific)
- Estimation & inference
- Robustness & heterogeneity
- Reproducibility discipline
- Theory papers
- Standard-error decision grid (the first thing a JFQA referee checks)
- Worked pass: a corporate-finance panel (numbers illustrative)
- Filter log the referee will try to reconstruct
- Execution bridge (StatsPAI / Stata MCP)
- Output format
What does the jfqa-data-analysis skill do?
Use when running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES), winsorizing, fixed effects, clustered and Newey-West standard errors, robustness, and heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code. For theory papers, lighten this and document numerical examples instead.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfqa-data-analysis --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.