jcf-data-analysis
Use when building and running the empirical analysis for a Journal of Corporate Finance (JCF) paper — assembling WRDS-era firm panels (Compustat/CRSP/SDC/DealScan), constructing variables, estimating with fixed effects and clustered errors, and layering robustness. It guides the empirical build; pair it with jcf-identification-strategy for the design itself.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jcf-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.
# Data Analysis (jcf-data-analysis) ## When to trigger - Assembling the firm-level panel and constructing corporate-finance variables - Choosing estimators, fixed effects, and clustering - Planning the robustness battery a JCF referee will expect ## Data build (empirical corporate finance) - Core sources: **Compustat** (fundamentals, leverage, payout), **CRSP** (returns/event windows), **CCM** (linked), **SDC/Refinitiv** (M&A, IPO/SEO, repurchases), **DealScan** (loans/covenants), **BoardEx/ISS/Execucomp** (governance, pay). Respect WRDS/vendor licenses — never redistribute raw vendor data. - Document **sample construction** (filters, financials/utilities exclusions, fiscal-year alignment), **winsorizing** (e.g., 1/99), and every variable's formula. ## Estimation conventions - **High-dimensional fixed effects** (`reghdfe` / `fixest`): firm, year, and often industry×year. - **Cluster** standard errors at the firm level (and/or two-way firm-and-year) consistent with the variation. - For staggered-adoption DID, use **modern estimators** (see jcf-identification-strategy), not plain TWFE. - Report economic magnitudes, not just significance — JCF readers want the size of the effect on a
- When to trigger
- Data build (empirical corporate finance)
- Estimation conventions
- Robustness the referee expects
- Reproducibility (ties to data policy)
- Firm-panel audit ledger
- Variable-construction crosswalk (Compustat conventions)
- Worked build: a board-reform firm panel
- Estimation pushback JCF referees raise
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output
What does the jcf-data-analysis skill do?
Use when building and running the empirical analysis for a Journal of Corporate Finance (JCF) paper — assembling WRDS-era firm panels (Compustat/CRSP/SDC/DealScan), constructing variables, estimating with fixed effects and clustered errors, and layering robustness. It guides the empirical build; pair it with jcf-identification-strategy for the design itself.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jcf-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.