# Jar Tables Figures > 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: `thedixitjain/jar-tables-figures` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/jar-tables-figures` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/jar-tables-figures/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/jar-tables-figures --- # 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`](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md). ## 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 - [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — official JAR/Chicago Booth/Wiley URLs (accessed 2026-06-01) - [`../../resources/external_tools.md`](../../resources/external_tools.md) — exhibit-export tooling (estout / outreg2 / modelsummary) and data sources --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Accounting-Research-Skills/skills/jar-tables-figures/SKILL.md`