ectheory-tables-figures

GitHub

用于生成计量理论论文中的蒙特卡洛模拟表格与诊断图。涵盖大小/功效表、覆盖率及收敛性绘图,遵循自包含注释、诚实对比及50%缩小可读性等标准,确保符合期刊TIFF/EPS/PDF格式规范。

Econometric-Theory-Skills/skills/ectheory-tables-figures/SKILL.md brycewang-stanford/Awesome-Journal-Skills

Trigger Scenarios

将蒙特卡洛输出转换为发表用表格(如大小、功效、覆盖率) 构建有限样本分布或极限行为收敛的图表 准备符合ET格式和分辨率要求的图形文件 检查附录是否自包含且在打印缩小时清晰可读

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ectheory-tables-figures -g -y
More Options

Non-standard path

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Econometric-Theory-Skills/skills/ectheory-tables-figures -g -y

Use without installing

npx skills use brycewang-stanford/Awesome-Journal-Skills@ectheory-tables-figures

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ectheory-tables-figures -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "ectheory-tables-figures",
    "description": "Use for the exhibits in an Econometric Theory (ET) paper — Monte Carlo size\/power\/coverage tables, finite-sample and limit-behavior plots, with self-contained notes, ET figure-format specs (TIFF\/EPS\/PDF), and legibility at 50% reduction."
}

Tables & Figures (ectheory-tables-figures)

When to trigger

  • Turning Monte Carlo output into publication tables (size, power, coverage, bias, RMSE)
  • Building plots of finite-sample distributions, convergence to the limit, or rejection curves
  • Preparing figure files to ET's format and resolution specs
  • Checking that exhibits are self-contained and legible after print reduction

What ET exhibits look like

ET is a theory journal, so most exhibits are simulation tables and diagnostic plots that illustrate the asymptotics, not empirical-result tables. Common exhibits:

  • Size/power tables — empirical rejection rates under the null (vs nominal level) and under alternatives, across DGPs and sample sizes.
  • Coverage / bias / RMSE tables — for estimators, across n and DGP, against a benchmark method.
  • Distribution / convergence plots — empirical vs limiting density (e.g., normal, mixed-normal, Brownian functional); QQ plots; rejection curves as a function of the alternative.
  • Rate plots — a statistic against n on a scale that reveals the convergence rate.

Construction standards

  • Self-contained notes. Each table/figure note states the DGP, sample sizes, number of replications, nominal level, and what each column/curve is — readable without the main text.
  • Honest contrast. Put your method beside the natural competitor in the same table; do not hide the comparison.
  • Legibility. Exhibits must be legible at 50% reduction; avoid chartjunk, 3D, and dense color. Keep table precision sensible (e.g., 3 decimals for rejection rates).
  • Figure files (ET specs). Supply TIFF (line art >=600 dpi, greyscale >=300 dpi), EPS (fonts embedded), or PDF. Embed fonts; vector output for line art.
  • Numbering. Tables and figures numbered and called out in order in the text.

Checklist

  • Each exhibit note states DGP, n, replications, nominal level, and column/curve definitions
  • Size reported against the nominal level; power against stated alternatives
  • Benchmark method shown alongside, not omitted
  • Plots show convergence/limit behavior clearly; rate legible where claimed
  • Legible at 50% reduction; no chartjunk; sensible decimal precision
  • Figure files in TIFF/EPS/PDF with fonts embedded, at required resolution
  • All exhibits numbered and referenced in order

Anti-patterns

  • A size table without the nominal level, so over-/under-rejection cannot be judged
  • Notes that require the reader to hunt through the text to decode the columns
  • Cherry-picked DGPs hiding the regime where the method underperforms
  • Raster figures at low dpi that blur at print reduction
  • 3D bars or rainbow palettes obscuring a simple size/power comparison

Exhibit pass for Econometric Theory

Treat this skill as an executable review pass, not a prose hint. First lock the primitive assumptions, theorem statement, proof route, and example showing why the result matters; then judge whether the current manuscript answers the venue's real reader: econometric theorists who read for assumptions, theorem novelty, proof architecture, and relation to known asymptotics.

  • Do the pass: For every table or figure, state the estimand or object, sample or case base, uncertainty display, and one sentence the exhibit proves for the venue audience.
  • Return a ledger: give claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
  • Sibling guard: compare against Journal of Econometrics for applied-method reach, Quantitative Economics for theoretical economics, Econometrica for general theory-plus-economics contribution; if a sibling owns the contribution, recommend re-routing before polishing format.
  • Stop condition: do not give submission-ready advice until the pack's resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.

Output format

【Exhibit】size / power / coverage / bias-RMSE / distribution / rate plot
【Self-contained note】DGP + n + reps + level stated? [Y/N]
【Benchmark shown】[Y/N]
【Legible at 50%】[Y/N]
【File format】TIFF / EPS / PDF, fonts embedded? [Y/N]
【Next step】ectheory-writing-style

Version History

  • 1839142 Current 2026-07-05 12:52

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