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Jar Tables Figures

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jar-tables-figures

Jar Tables Figures is an Agent Skill for documents published in the brycewang-stanford/Awesome-Journal-Skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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).

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How to use this skill

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  2. Copy and paste the prompt below.
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Prompt to paste
Inspect the Agent Skill at https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-Accounting-Research-Skills/skills/jar-tables-figures/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/jar-tables-figures, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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

Resources