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Jar tables figures

Skill brycewang-stanford/Awesome-Journal-Skills/Journal-of-Accounting-Research-Skills/skills/jar-tables-figures

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-tables-figures

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Use when finalizing the descriptive, results, and identification exhibits for a Journal of Accounting Research (JAR) manuscript — the summary-statistics table, correlation matrix, main regression tables, and identification plots that an empirical-archival accounting paper lives or dies on. Builds the exhibits; it does not run the estimation (jar-data-analysis) or polish prose (jar-writing-style).

SKILL.md

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Tables & Figures (jar-tables-figures)

When to trigger

  • Tables are cluttered, inconsistent, or not self-explanatory
  • A referee cannot tell the sample, units, or SE clustering from the table notes
  • You need the standard JAR exhibit set assembled in house style
  • Identification needs a figure (pre-trends, RD plot) to be believed

The standard JAR exhibit set

An empirical-archival JAR paper is read through its tables. Build, in order:

  1. Sample construction table — from the raw population to the final N, line by line, with each screen and the observations lost. Referees expect to trace the sample.
  2. Descriptive statistics — N, mean, SD, and key percentiles for every variable; state winsorization (e.g., 1/99%).
  3. Correlation matrix — Pearson (and often Spearman) among the main variables; flag significance.
  4. Main results — the central regression(s): coefficients with t-/z-stats or standard errors beneath, the SE clustering stated in the note, fixed effects indicated, and N and R² (or pseudo-R²) reported.
  5. Identification & robustness — first-stage (IV), DiD dynamics, RD estimates, falsification/placebo, alternative measures, and cross-sectional (channel) partitions.

House-style discipline

JAR uses a custom author-date house style; match the typographic conventions of recent JAR articles rather than importing a reference manager's defaults. For exhibits specifically:

  • Self-contained: a title, the sample/period, the units, and the dependent variable are clear from the table and its note alone.
  • Inference visible: report what is beneath the coefficients (t-stats / SEs) and state the clustering in the note; mark significance consistently.
  • Variable definitions: every variable defined (often an appendix variable-definitions table) with its data source (Compustat/CRSP/I/B/E/S/Audit Analytics/EDGAR).
  • Numbers consistent: Ns, coefficients, and signs match the text; decimal places consistent.

Figures that earn their place

Use figures where they do identification work a table cannot: DiD event-study plots (coefficients by period with confidence bands, showing flat pre-trends), RD plots (binned means around the cutoff), and time series of the treatment/setting. Avoid decorative charts; every figure should support the causal claim.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: execution-with-mcp. JAR is archival/empirical accounting; foreground identification around disclosure and regulation shocks, with modern DiD where adoption is staggered.

  • Tables: etable (multi-model columns) or did_summary_to_latex straight from the result_id.
  • Figures: plot_from_result / enhanced_event_study_plot / event_study_table — axis units and the SE/clustering note baked in.
  • Every note names the estimator + clustering and states the effect size in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Checklist

  • Sample-construction table traces raw population → final N
  • Descriptives with winsorization stated; correlation matrix included
  • Main table reports coefficients, inference statistics, FE, N, R²
  • SE clustering stated in every regression-table note
  • Variable-definitions table with data sources included
  • Identification figure (pre-trends / RD) present where the claim is causal
  • All numbers reconcile with the text; formatting matches recent JAR articles

Anti-patterns

  • Mystery samples: a final N with no construction table.
  • Naked coefficients: no SEs/t-stats and no clustering note.
  • Reference-manager defaults instead of JAR house style.
  • Decorative figures that do no identification work.
  • Undefined variables or sources scattered through the text.

Output format

【Exhibit set】sample / descriptives / correlations / main / robustness present?
【Inference shown】t-stats or SEs + clustering stated in notes?
【Variable definitions】table with sources included?
【Identification figure】pre-trends / RD plot present where causal?
【Consistency】Ns and coefficients reconcile with text?
【House style】matches recent JAR articles?
【Next step】jar-writing-style

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