| name | revacc-data-analysis |
| description | 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). |
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.
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 instrument strength (e.g., F-statistic) and defend the exclusion in words.
- Event studies: report abnormal returns with a defensible benchmark and window, and confront confounding events — central for value-relevance and information-content claims.
Measure accounting constructs credibly
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).
The robustness battery referees expect
- Alternative specifications: controls in/out, alternative fixed effects, alternative key-construct measures.
- Subsamples and falsification/placebo tests (effect absent where theory says it should be).
- Sensitivity of identification assumptions (alternative instruments, donut RD, bounds).
- Cross-sectional partitions that confirm the predicted channel (the conditional predictions from
revacc-theory-development).
- Sample-construction, winsorization, and screen choices documented and varied.
Provenance is a deliverable, not a courtesy
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).
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. RAST is empirical accounting; emphasize identification of disclosure / governance effects and the multiple-testing haircut for mined associations.
- Many outcomes / specifications:
romano_wolf (step-down FWER) or
benjamini_hochberg — report the adjusted threshold.
- OVB sensitivity:
oster_delta / sensemakr.
- Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley;
multilevel data → cluster at the right level.
- Re-fit off one handle:
audit_result(result_id) lists the missing checks and the
exact suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the
executed chain in the JF execution walkthrough.
Checklist
Anti-patterns
- White/robust SEs on panel data ignoring within-firm correlation.
- Naive two-way-FE DiD with staggered adoption.
- One proxy, no validation for a contested accounting construct.
- Significance fishing across windows, bandwidths, or specifications.
- Confounded event windows in value-relevance/information-content tests.
- Black-box sample: screens and vintages undocumented, results unreproducible.
Output format
【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
