jae-data-analysis
Use when running and reporting the empirical analysis for a Journal of Accounting and Economics (JAE) manuscript — building the archival sample, choosing fixed effects and clustered standard errors, executing the identification design, and demonstrating robustness for large-sample capital-markets/contracting/disclosure data. Executes and reports the analysis; it does not design the study (jae-methods) or frame the contribution (jae-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jae-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 & Inference for JAE (jae-data-analysis) ## When to trigger - The sample is built and it is time to estimate and report - You are unsure how to specify fixed effects or cluster standard errors - Reviewers will probe endogeneity, correlated omitted variables, or sample selection - A reviewer says "the standard errors are understated" or "this is not identified" ## Build and document the archival sample first JAE reviewers expect a transparent **sample-construction waterfall**: starting population (e.g., Compustat firm-years), each merge (CRSP, I/B/E/S, Execucomp, DealScan, Audit Analytics via WRDS), each exclusion (financials/utilities, missing data, penny stocks), and the final N at every step. Report descriptive statistics and a correlation table. **Winsorize** continuous variables (commonly at 1%/99%) and say so. ## Specify the estimator to match the panel and the design | Data structure / claim | Estimator / specification | |-----------------------------------------------|-------------------------------------------------------------| | Firm panel with unobserved heterogeneity | Firm and year fixed effects (e.g., `reghdfe`) | | Inference with within-firm correlatio
- When to trigger
- Build and document the archival sample first
- Specify the estimator to match the panel and the design
- Execute the identification, not just the regression
- Robustness (expected, not optional)
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the jae-data-analysis skill do?
Use when running and reporting the empirical analysis for a Journal of Accounting and Economics (JAE) manuscript — building the archival sample, choosing fixed effects and clustered standard errors, executing the identification design, and demonstrating robustness for large-sample capital-markets/contracting/disclosure data. Executes and reports the analysis; it does not design the study (jae-methods) or frame the contribution (jae-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jae-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.