Individual skill
revacc-data-analysis
Use when running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering, executing the identification, validating accounting constructs, and the robustness battery referees expect, plus the data provenance trail. Executes and reports; it does not choose the identification strategy (revacc-methods) or frame the contribution (revacc-contribution-framing).
Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis/SKILL.md
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- Last checked
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- Sep 27, 2026
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- MIT
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# Data Analysis & Robustness (revacc-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 accounting panel
- Referees will probe endogeneity, construct measurement, or the channel
- You must document the Compustat/CRSP/I/B/E/S/audit-data provenance behind the sample
- An analytical paper needs a stylized empirical illustration of its comparative statics
## Get the standard errors right (a RAST 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** (e.g., a state- or country-level policy), 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 — an unjustified SE choice is a fast credibility hit at a journal that often decides in one round.
