jar-data-analysis
Use when running and reporting the empirical-archival analysis for a Journal of Accounting Research (JAR) manuscript — standard-error clustering, endogeneity execution, construct measurement, and the robustness battery referees expect, plus the reproducible data-and-code package JAR requires. Executes and reports; it does not choose the identification strategy (jar-methods) or frame the contribution (jar-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jar-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 & Robustness (jar-data-analysis) ## When to trigger - Data are built and it is time to estimate and report - You are unsure how to cluster standard errors for your panel - Referees will probe endogeneity, measurement, or the channel - You must assemble the reproducible **data-and-code package** JAR posts ## Get the standard errors right (a JAR signature check) Empirical-accounting referees scrutinize inference. Default to **clustering by firm**, and consider **two-way clustering by firm and year** (Petersen) when both cross-sectional and time-series dependence are present. With **few clusters**, use the **wild-cluster bootstrap** rather than asymptotic cluster-robust SEs. Match the clustering to the source of correlated shocks implied by your design, and report the choice explicitly. ## Execute the identification, don't just assert it - **DiD**: report pre-trends and use heterogeneity-robust estimators for staggered timing (Callaway-Sant'Anna / Sun-Abraham), not naive two-way FE. - **RD**: report the optimal bandwidth, robust bias-corrected estimates, a manipulation (density) test, and covariate balance at the cutoff. - **IV/2SLS**: report the first stage and instru
- When to trigger
- Get the standard errors right (a JAR signature check)
- Execute the identification, don't just assert it
- Measure accounting constructs credibly
- The robustness battery referees expect
- Reproducibility is a deliverable, not a courtesy
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
- Resources
What does the jar-data-analysis skill do?
Use when running and reporting the empirical-archival analysis for a Journal of Accounting Research (JAR) manuscript — standard-error clustering, endogeneity execution, construct measurement, and the robustness battery referees expect, plus the reproducible data-and-code package JAR requires. Executes and reports; it does not choose the identification strategy (jar-methods) or frame the contribution (jar-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jar-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.