Tables & Figures (aejmac-tables-figures)
SkillAI & modelsThis skill helps you build and revise the exhibits in an American Economic Journal: Macroeconomics manuscript. It covers impulse-response figures, fan charts, model-fit overlays, and regression or moment tables, shaping them to AEA house standards and macro conventions. The focus is on formatting and clarity, so your exhibits look right and read easily.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding it, share the figure or table you are working on and ask it to build or revise the exhibit to AEJ: Macro standards.
Then ask your AI: use the Tables & Figures (aejmac-tables-figures) skill
What your AI can do with it
- Build impulse-response figures that follow macro conventions
- Format fan charts to AEA house standards
- Clean up model-fit overlays for clarity
- Lay out regression and moment tables in journal style
- Revise existing exhibits so they match AEA expectations
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in AEJ-Macroeconomics-Skills/skills/aejmac-tables-figures/SKILL.md and read by ahel’s review.
When to trigger
- IRF figures lack confidence bands, units, or a clear horizon axis
- Tables are dense, mislabeled, or report coefficients without the macro quantity of interest
- A model-fit comparison (data vs. simulated moments) is asserted in text but not shown
- Exhibits do not stand alone — the reader needs the body text to decode them
The AEJ: Macro exhibits bar
AEJ: Macro follows AEA house style (applied by the AEA at copyediting; the submitted PDF need not pre-conform, but clean exhibits speed review). Macro adds its own conventions: the impulse-response function is the workhorse figure, and the headline quantity (multiplier, peak response, welfare cost, share of variance) must be readable off the exhibit. Every exhibit should be self-contained: title, axis labels with units and horizon, sample, and the inference object (bands / SE) in the note.
Figure conventions (macro-specific)
- IRFs: plot the response with a shaded confidence band (state the level, e.g., 68% and/or 90%); label the horizon axis in the natural unit (quarters/months/years); mark zero; if sign-restricted, show the identified set / median-target, not a spurious point.
- Fan charts / forecast bands: show the full predictive distribution where the claim is about uncertainty.
- Model-fit overlays: plot data vs. model (targeted and untargeted moments) on the same axes; this is the credibility figure for quantitative work.
- Counterfactual figures: baseline vs. counterfactual paths with uncertainty carried through.
- Variance decompositions / contribution plots: stacked or grouped, with a clear legend.
- Vector output (
.eps/.pdf) preferred for final files; keep colors legible in grayscale; no chartjunk.
Table conventions
- Report the economic quantity, not just raw coefficients — e.g., the multiplier, the elasticity, the implied share — with its SE/band.
- AEA tables conventionally use significance stars; AEJ: Macro accepts them, but the standard error / band must be present and must carry the inference (do not let stars substitute for a reported SE). Note significance levels in the table note.
- One decisive number per row; align decimals; units in the column header or note.
- Targeted-vs-untargeted-moment tables: clearly flag which moments were matched.
- Notes carry: sample, frequency, estimator, inference (HAC/cluster), and what an asterisk means.
Main text vs. online appendix
- Main text: the decisive IRFs, the headline table, the model-fit figure. AEJ: Macro articles run ~40 pages including exhibits (检索于 2026-06;以官网为准), so exhibits compete for space.
- Online appendix: full robustness IRFs, additional moments, alternative specifications, derivations.
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) ordid_summary_to_latexstraight from theresult_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 IRF has a confidence band with its level stated, a labeled horizon axis, and a zero line
- Sign-restricted responses shown as a set/median-target, not a fake point estimate
- Model-fit figure (data vs. model, targeted + untargeted) present for quantitative papers
- Tables report the macro quantity with its SE/band; notes give sample/estimator/inference
- Significance stars (if used) accompany, not replace, reported SEs
- Each exhibit is self-contained (decodable without the body text)
- Decisive exhibits in main text; the rest in the online appendix; figures vector + grayscale-legible
Anti-patterns
- IRFs with no confidence bands, or bands whose level is never stated
- Sign-restricted IRFs drawn as a single line, hiding set-identification
- Tables of raw VAR coefficients with no impulse responses or implied quantity
- Stars reported but standard errors omitted
- A model "matches the data" claimed in prose with no overlay figure
- Exhibits readable only with a magnifying glass after the AEA's two-column typesetting
Output format
【Exhibit inventory】IRFs / fan charts / model-fit / counterfactual / tables
【IRF compliance】bands+level / horizon axis / zero line / set-vs-point? [Y/N]
【Quantity legibility】headline number readable off each exhibit? [Y/N]
【Table compliance】SE/band present; notes complete; stars don't replace SEs? [Y/N]
【Main vs. appendix split】decisive in text, rest in online appendix? [Y/N]
【Next step】aejmac-writing-style
Signals
- GitHub stars
- 1k
- Forks
- 146
- Last commit
- Aug 2026
Advanced
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- skill
- Gateway key
aejmac-tables-figures- Source
- github.com/brycewang-stanford/awesome-journal-skills