本文へ移動
cccskills
無料GitHub で公開

aejmic-tables-figures

Use when presenting results for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — propositions, numerical examples, schematic theory figures, and empirical/experimental tables. Builds exhibits to AEA house norms; it does not derive the results (see aejmic-theory-model).

インストール方法を見る

含まれるファイル(1)

  • SKILL.md5.7 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Result Presentation: Propositions, Examples & Exhibits (aejmic-tables-figures)

When to trigger

  • Propositions are buried in prose or stated imprecisely
  • A numerical example would make an abstract result concrete but is missing or cluttered
  • A schematic figure (timeline, type space, equilibrium region) would help but is not drawn
  • (Applied/experimental) tables are dense or carry significance asterisks

Presenting a theory-first result

AEJ: Micro results are mostly propositions, characterizations, and numerical examples, not regression tables. The exhibit job is to make a formal result legible and concrete.

Propositions and theorems

  • State each as a numbered, self-contained Proposition/Theorem/Lemma: a reader should grasp it without re-reading the model.
  • Put the economic content first in surrounding prose ("Higher search costs raise equilibrium prices because…"), then the formal statement.
  • Define every symbol at first use; keep notation consistent across statements; minimize ad-hoc subscripts.

Numerical examples (the AEJ: Micro workhorse)

  • Use a small, transparent parameterization to illustrate the mechanism, not to "test" it — say which proposition it instantiates.
  • Show the moving part: a table or plot of the key object (optimal mechanism, equilibrium strategy, welfare) as a primitive varies.
  • Report enough to reproduce (parameter values, grid) — these go in the replication package.

Schematic theory figures

  • Timelines for extensive-form games; type-space / signal partitions for information design; equilibrium-region plots in parameter space; best-response diagrams.
  • Vector output; readable labels; no chartjunk. A figure should carry one idea.

Applied / experimental tables

  • Three-line tables; report standard errors and confidence sets, not significance asterisks (AEA house norm for inference presentation).
  • Place exhibits near their discussion; self-contained notes (sample, units, what each column is).

Choosing the right exhibit for the result

What you want to conveyBest exhibit
A characterization holdsA numbered Proposition + an intuition sentence; rarely a table
How an optimum moves with a primitiveA numerical-example plot (object vs. primitive)
The structure of an equilibriumA schematic (timeline, partition, region plot)
A magnitude from estimation/experimentA three-line table with SEs / coverage sets
Model fit (structural)Data vs. model overlay, not a fit statistic alone

Pick one exhibit per idea. If a sentence does the job, do not add a figure; an AEJ: Micro theory paper can be exhibit-light without penalty.

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.

  • Tables: etable (multi-model columns) or did_summary_to_latex straight from the result_id — one variable definition, one set of numbers, body and appendix in sync.
  • 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 (from the result's diagnostics) and states the magnitude in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Checklist

  • Every formal result is a numbered, self-contained Proposition/Theorem/Lemma
  • Economic content precedes the formal statement in prose
  • Notation defined at first use and consistent across exhibits
  • Numerical example states which proposition it illustrates and is reproducible
  • Schematic figures carry one idea each; vector output; clean labels
  • (Applied) tables report SEs / coverage sets, no significance asterisks
  • Exhibits placed near discussion; notes self-contained

Anti-patterns

  • A proposition stated only inside a paragraph, un-numbered and un-restated
  • A numerical example with a dozen parameters and no statement of what it shows
  • Overloaded figures (multiple unrelated panels, dense legends, chartjunk)
  • Inconsistent or ad-hoc notation across statements
  • Significance asterisks in an empirical/experimental table

Worked vignette (illustrative)

An information-design paper proves the sender-optimal signal is a two-message partition. The presentation: state Proposition 2 cleanly ("The sender-optimal signal partitions the state into {low, high} with cutoff x*"), precede it with the intuition (pooling above x* maximizes the receiver's action while preserving credibility), then a schematic figure of the state line with the cutoff and a small numerical example showing how x* moves with the sender's bias. No regression table, no asterisks — the exhibits make the mechanism visible.

Output format

【Formal results】numbered propositions/theorems, each self-contained? [Y/N]
【Economic-content-first prose】present for each? [Y/N]
【Numerical example】illustrates which proposition; reproducible? [Y/N]
【Schematic figures】one idea each; vector; clean labels? [Y/N]
【Applied tables】SEs/coverage, no asterisks? [Y/N or N/A]
【Next step】aejmic-writing-style

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

brycewang-stanford のスキルをすべて見る

このスキルの問題を報告する