| name | jar-data-analysis |
| description | 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). |
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 instrument strength (e.g., F-statistic), and defend the exclusion restriction in words.
- Event studies: report abnormal returns with a defensible benchmark model and window, and confront confounding events.
Measure accounting constructs credibly
Use measures with precedent in prior JAR/JAE work (discretionary accruals, earnings persistence/smoothness, disclosure indices, comparability, audit-quality proxies, bid-ask spread / PIN for information asymmetry). Show the proxy behaves sensibly (validation, correlations with established measures) and test sensitivity to alternative proxies — proxy fragility is a common rejection reason.
The robustness battery referees expect
- Alternative specifications: controls in/out, alternative fixed effects, alternative measures of the key construct.
- Subsamples and falsification/placebo tests (e.g., 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 jar-theory-development).
- Sample-construction and winsorization choices documented and varied.
Reproducibility is a deliverable, not a courtesy
JAR's data-and-code sharing policy requires and hosts the materials. Keep top-to-bottom runnable scripts that regenerate every table and figure from raw extracts; document screens, vintages, and access dates; respect the terms of use (academic-research-only, acknowledgement of the JAR publication and code authors, authors retain copyright). On a Registered Report, the executed analysis must match the pre-approved Stage 1 protocol.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JAR is archival/empirical accounting; foreground identification around disclosure and regulation shocks, with modern DiD where adoption is staggered.
- 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.
- "Code available on request" — JAR requires the package to be posted.
Output format
【Estimator & SEs】model; clustering (firm / firm×year / wild bootstrap)
【Identification executed】diagnostics reported (pre-trends/bandwidth/first-stage)
【Construct measurement】proxy + validation + alt-proxy robustness
【Robustness/falsification】[...]
【Channel partitions】conditional predictions confirmed? [...]
【Reproducibility】data/code package status per JAR policy
【Open issues for referees】[...]
【Next step】jar-contribution-framing
Resources
