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Jar data analysis

Skill brycewang-stanford/Awesome-Journal-Skills/Journal-of-Accounting-Research-Skills/skills/jar-data-analysis

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill jar-data-analysis

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What its author says it does

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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).

SKILL.md

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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

  • 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

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