# Jcf Tables Figures > Use when building the tables and figures for a Journal of Corporate Finance (JCF) empirical paper — summary statistics, main regression tables with fixed effects and clustering disclosed, event-study/CAR plots, and self-contained notes. It shapes the exhibits; pair with jcf-data-analysis for the underlying estimates. - Skill: `thedixitjain/jcf-tables-figures` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/jcf-tables-figures` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/jcf-tables-figures/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/jcf-tables-figures --- # Tables & Figures (jcf-tables-figures) ## When to trigger - Designing the summary-statistics, main-result, and robustness tables - Building event-study or CAR figures with confidence bands - Writing self-contained table/figure notes a reviewer can read without the text ## Table conventions (empirical corporate finance) - **Summary statistics**: N, mean, SD, key percentiles; flag winsorizing and units. Match the sample to the regression sample. - **Main regression tables**: coefficients with standard errors (or t-stats) clearly labeled; report the **fixed effects included**, the **clustering level**, N, and within/adjusted R². Show **economic magnitude** (e.g., a 1-SD change) near the headline coefficient. - **Robustness tables**: vary one thing per panel/column (definition, subsample, FE, estimator) so the reader sees what moves the result. - Use a consistent decimal precision; do not hide the dependent variable's scaling. ## Figure conventions - **Event-study / CAR plots**: plot coefficients (or cumulative abnormal returns) with **confidence bands**; mark the event date; show **pre-event leads** to support parallel trends. - Binned scatters for nonlinearity; avoid 3D, gradients, and chartjunk. - **Vector output (PDF/EPS)** for print resolution. ## Self-contained notes (required) Each exhibit's note states: sample and period, variable definitions or a pointer, **fixed effects**, **clustering**, winsorizing, and significance markers. A referee should not need the body text to read the table. ## Formatting and policy - "Your paper, your way" at first submission means exhibit styling need only be **consistent**; full Elsevier styling comes at revision. - Tables/figures count toward the paper's exhibits but JCF states **no fixed length ceiling** (待核实) — still, keep the set tight and non-redundant. ## Exhibit architecture (calibration, hedged) Accepted empirical JCF papers tend to follow a recognizable arc — treat the counts as calibration, not rules, and confirm against the journal's current author guidelines: - Table 1: sample construction and/or summary statistics on the regression sample. - Table 2: main fixed-effects estimates, sparse controls first, saturated last. - Tables 3–4: identification exhibits — event-study dynamics, pre-trends, first stage, or density tests. - Tables 5+: mechanism splits and robustness, one varied dimension per panel. - Figure 1 is frequently the event-study or RDD plot — many JCF readers judge identification from this figure alone. - Six to nine main exhibits is a common footprint; overflow robustness belongs in an appendix or internet appendix numbered IA.1, IA.2, and cited by number in the text. ## Worked headline-table sketch Illustrative skeleton for a governance-shock paper (numbers invented): ```text (1) (2) (3) Treated x Post 0.014** 0.015** 0.013** (0.006) (0.006) (0.006) Firm FE Y Y Y Year FE Y — — Industry x Year FE — Y Y Controls — — Y N 21,043 21,043 20,877 Within R² 0.08 0.11 0.12 Magnitude: 1 SD of treatment exposure ≈ 12% of mean investment ``` The magnitude line beneath the table body is the JCF habit worth copying — it answers "is this economically big?" before the referee asks. ## Exhibit pushback and the fix - "I cannot tell what is clustered." → Put FE and clustering rows in every regression table, not in a global footnote. - "The event-study figure has no bands." → Re-plot with 95% CIs and at least four pre-period leads visible. - "Summary stats do not match the regression N." → Recompute Table 1 on the estimation sample; reconcile any drop in a sample-construction panel. - "Too many tables." → Merge robustness variants into panels; push the remainder to the internet appendix and cite IA numbers in the text. ## Execution bridge (StatsPAI / Stata MCP) Generate exhibits from the fitted result, not by retyping numbers. Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). JCF is corporate finance — endogeneity of corporate policies is the central threat; foreground IV/DiD identification. - **Tables:** `etable` (multi-model) 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 magnitude 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). ## Anti-patterns - A regression table that hides the FE and clustering structure. - "Stars only" with no economic magnitude. - Event-study plots without confidence bands or pre-trend leads. - Summary stats computed on a different sample than the regressions. ## Output ``` 【Tables】headline FE/cluster/N disclosed? [Y/N]; magnitude shown? [Y/N] 【Figures】event/CAR with CIs + leads? [Y/N]; vector output? [Y/N] 【Notes】each exhibit self-contained? [Y/N] ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Corporate-Finance-Skills/skills/jcf-tables-figures/SKILL.md`