jar-data-analysis
GitHub用于执行并报告JAR期刊实证分析，涵盖标准误聚类、内生性检验、度量构建及稳健性测试。确保结果符合审稿要求，并生成可复现的数据代码包，但不负责识别策略选择或贡献框架撰写。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jar-data-analysis -g -y
SKILL.md
Frontmatter
{
"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) orbenjamini_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 exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom 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
- SE clustering matches the design (firm / firm-and-year; wild bootstrap if few clusters)
- Identification executed with diagnostics (pre-trends / bandwidth / first-stage / balance)
- Modern DiD estimators used for staggered treatment timing
- Key construct validated; results robust to alternative proxies
- Robustness, falsification/placebo, and channel partitions reported
- Winsorization/sample screens documented and varied
- Reproducible data-and-code package assembled per JAR policy
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
../../resources/official-source-map.md— official JAR/Chicago Booth/Wiley URLs (accessed 2026-06-01)../../resources/external_tools.md— econometric packages (reghdfe / fixest / csdid / rdrobust / boottest) and data sources
Version History
- 1839142 Current 2026-07-05 13:24
