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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).
Journal-of-Accounting-Research-Skills/skills/jar-data-analysis/SKILL.md
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# 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