From review-of-accounting-studies-skills
Executes and reports empirical analysis for RAST accounting manuscripts: standard-error clustering, identification execution, construct validation, and robustness tests.
How this skill is triggered — by the user, by Claude, or both
Slash command
/review-of-accounting-studies-skills:revacc-data-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- Data are built and it is time to estimate and report
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.
Use measures with precedent in prior RAST/JAR/JAE/TAR work (discretionary accruals, accruals quality, earnings persistence/smoothness, disclosure indices, comparability, audit-quality proxies, bid-ask spread / PIN for information asymmetry). Show the proxy behaves sensibly (validation, correlation with established measures) and test sensitivity to alternative proxies — proxy fragility is one of the most common RAST rejection reasons. For analyst/forecasting work, document I/B/E/S handling (actuals definition, stale-forecast screens, splits adjustments).
revacc-theory-development).RAST does not run JAE's mandatory archive or JAR's posted package as the headline, but referees and the editor still expect a credible, reconstructable sample. Keep top-to-bottom runnable scripts that regenerate every table from raw extracts; document screens, vintages, and access dates for each source; respect database terms of use. If the work entered through the RAST Conference path, keep the version history clean for the conference-issue timeline. Confirm current data/code expectations on the official page (待核实; 检索于 2026-06).
【Estimator & SEs】model; clustering (firm / firm×year / wild bootstrap) + justification
【Identification executed】diagnostics reported (pre-trends/bandwidth/first-stage/balance)
【Construct measurement】proxy + validation + alt-proxy robustness
【Robustness/falsification】[...]
【Channel partitions】conditional predictions confirmed? [...]
【Provenance】sources/vintages/screens documented; scripts runnable
【Open issues for referees】[...]
【Next skill】revacc-contribution-framing
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin review-of-accounting-studies-skillsRuns and reports empirical archival analysis for JAR manuscripts: standard-error clustering, endogeneity, construct measurement, robustness battery, and reproducible data-and-code package.
Runs and reports empirical analysis for JAE manuscripts: builds archival samples, specifies fixed effects and clustered standard errors, executes identification (DiD, IV, matching), and demonstrates robustness.
Designs causal identification strategies for accounting research manuscripts, including DiD, IV, RDD, and event studies. Activates when research design is the bottleneck.