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Ecta tables figures

Skill brycewang-stanford/Awesome-Journal-Skills/Econometrica-Skills/skills/ecta-tables-figures

Use when finalizing the simulation tables and any empirical / illustrative exhibits in an Econometrica manuscript so each is self-contained, precise, and reproducible. Formats and audits exhibits; it does not design the Monte Carlo (use ecta-robustness) or run the estimation.From its SKILL.md

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill ecta-tables-figures

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

SKILL.md

5.8 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

Tables and Figures (ecta-tables-figures)

When to trigger

  • A Monte Carlo or empirical table is overloaded, ambiguous, or not self-contained
  • Notes do not say what is being reported (size? coverage? bias?) at what sample size and nominal level
  • Figures (power curves, coverage plots, value-function illustrations) lack axis labels or a legend
  • Numbers in the table cannot be traced to a specific script / seed

Econometrica exhibits are read closely by referees who will recompute or sanity-check entries — and at conditional acceptance the ES Data Editor re-runs the code behind every table, figure, and number that comes from data or simulation. Every table and figure must stand alone and trace to a seeded script in the replication package. Two Econometrica specifics that differ from applied siblings: (1) the 45-page main-text limit (incl. references and appendices) forces ruthless triage — keep the headline size/power/coverage tables in the body and push secondary designs to the Supplemental Material; (2) reproducibility is enforced by the Econometric Society Data Editor with a Zenodo deposit, not the AEA/openICPSR pipeline the AER/AEJ packs assume.

Simulation-table standards

  1. Self-contained. The caption + notes must let a reader interpret every number without the body text: estimator(s), DGP / design, sample size(s), number of replications, nominal level, and what each column reports (bias, RMSE, empirical size, coverage, length).
  2. One concept per table where possible — do not mix size, power, and point-estimation quality in one undifferentiated block.
  3. Consistent precision. Choose decimal places deliberately (e.g., sizes to 3 decimals so 0.052 vs. 0.048 is visible) and keep them consistent down a column.
  4. Highlight the comparison. The reader should see at a glance that the proposed method controls size / has lower RMSE than the competitor — order columns/rows so the contrast reads.
  5. Monte Carlo error. Where a size or coverage near the nominal level carries the argument, make the simulation precision clear (replication count in notes, or a reported MC standard error).
  6. Three-line / booktabs style. Clean rules (top, mid, bottom), no vertical lines, no shading — the standard for formal economics journals.

Figures

  • Label everything: axes with units, every series, the nominal-level reference line on a size/coverage plot, the null on a power curve.
  • Power curves: plot rejection probability against the departure parameter; mark the null and the nominal level; use size-adjusted curves for fair comparison.
  • Coverage plots: show coverage across designs / sample sizes against the nominal target.
  • Theory illustrations: equilibrium / value function / comparative statics across the parameter range, with the relevant region annotated.
  • Vector format (PDF/EPS) so curves stay crisp; legible at print size; colorblind-safe and readable in grayscale.

Traceability

  • Each table/figure should be regenerated by a named script with a fixed seed (tie this to ecta-replication-package). Keep a map: Table N ↔ script ↔ seed ↔ output file.
  • Numbers in the text must match the tables exactly (no rounding drift between prose and table).

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers. Full map: execution-with-mcp. Econometrica publishes econometric theory and applied micro; the chain below serves its applied/empirical papers (weak-IV-robust and modern-DiD reporting expected) — pure theory uses its own apparatus.

  • 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.

Checklist

  • Caption + notes make each exhibit fully self-contained
  • DGP/design, n, replications, nominal level, and per-column meaning all stated
  • Decimal precision deliberate and consistent
  • Competitor methods included; the key comparison reads at a glance
  • Monte Carlo precision conveyed where size/coverage carries the argument
  • Three-line / booktabs style; no vertical rules or shading
  • Figures fully labeled; vector format; readable in grayscale
  • Every exhibit traces to a named script + seed; text numbers match tables

Anti-patterns

  • A size table with no nominal level stated, so 0.07 is uninterpretable
  • Mixing size, power, and bias in one block with no clear separator
  • Reporting power without indicating size, making the method look artificially good
  • Inconsistent decimals down a column, hiding the comparison
  • A figure with unlabeled axes or an unexplained second series
  • Numbers in the text that disagree with the table by a digit
  • A table no script can regenerate, so replication review cannot verify it

Output format

【Exhibit】Table/Figure N — [what it shows]
【Self-contained?】yes / no — missing: [...]
【Reports】bias / RMSE / size / power / coverage / length / illustration
【Design+n+reps+level stated】yes/no
【Style】booktabs three-line / needs fixing
【Traceable to script+seed】yes/no
【Next step】ecta-writing-style

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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