Skip to content
cccskills
FreePublished on GitHub

aejmac-literature-positioning

Use when the contribution of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript relative to the macro frontier is fuzzy, contested, or undersold. Frames the marginal contribution against quantitative-theory and identified-empirical literatures; it does not fix the identification or the model itself.

The English summary is being prepared. Showing the original description.

Run score Not tested

See how to install

Files included (1)

  • SKILL.md4.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Literature Positioning (aejmac-literature-positioning)

When to trigger

  • The introduction cites many papers but never says what is new
  • A referee could ask "how is this different from [the obvious prior paper]?" and you lack a one-line answer
  • The paper sits between a theory literature and an empirical literature and the bridge is unclear
  • The contribution is real but framed as incremental ("we extend X by adding Y")

The AEJ: Macro positioning bar

AEJ: Macro is a broad-interest macro journal, so positioning must locate the paper for a general macro reader, not only for the three people working on the exact model or shock series. Two literatures usually bound a macro paper, and you must place the contribution against both:

  • the quantitative-theory frontier (DSGE / HANK / heterogeneous-agent / structural) — what does your model deliver that the canonical model cannot?
  • the identified-empirical frontier (SVAR / local projections / narrative / proxy-VAR / micro-data macro) — what does your evidence pin down that prior designs left open?

A hybrid paper (model matched to identified evidence) must say why the combination answers a question neither side resolved alone — this is often the strongest AEJ: Macro contribution.

Positioning moves

Move 1 — Name the closest 2–3 papers, then the wedge

Do not list 30 citations. Name the 2–3 papers a referee would call "basically the same," and state in one sentence what your paper does that each cannot. The wedge should be a quantity, mechanism, or identification gain, not "a different dataset."

Move 2 — Place against both frontiers

  • vs. theory frontier: "The canonical [RBC/NK/HANK] model predicts/assumes Z; we relax/measure Z and the macro implication changes by ___."
  • vs. empirical frontier: "Prior [SVAR/LP] designs cannot separate channel A from channel B; our [narrative/high-frequency/sign-restriction] identification does."

Move 3 — State the contribution as a sentence with a quantity

"We show that [mechanism] accounts for [X%] of [aggregate], overturning/refining the prior view that ___." A magnitude makes the contribution legible to a general reader and to the AEA process.

Move 4 — Calibrate the claim

Say what the paper does not establish: the regime, sample, or country where the result holds, and what remains open. Over-claiming is the fastest path to a referee's "the contribution is oversold."

Checklist

  • The 2–3 nearest papers named, each with a one-line wedge
  • Positioned against the quantitative-theory frontier (what the model adds)
  • Positioned against the identified-empirical frontier (what the design pins down)
  • Hybrid papers: why model + evidence beats either alone
  • Contribution stated as one sentence with a quantity/mechanism
  • Scope limits stated; no over-claiming beyond regime/sample
  • Broad-interest framing: a general macroeconomist sees why it matters

Anti-patterns

  • A "literature wall" — long paragraphs of citations with no stated wedge
  • Positioning only against the subfield, invisible to general macro readers (reads as a field paper, wrong for AEJ: Macro)
  • "We are the first to…" claims that a single prior paper falsifies
  • Framing the contribution as a method tweak rather than a macro answer
  • Burying the contribution sentence on page 4 instead of paragraph 1–2 of the intro

Output format

【Nearest papers + wedge】1) ... 2) ... 3) ...
【Vs. theory frontier】what the model/structure adds
【Vs. empirical frontier】what the identification pins down
【Contribution sentence (with quantity)】...
【Scope / what it does NOT claim】...
【Next step】aejmac-identification (empirical) and/or aejmac-theory-model (quantitative)

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

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.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

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.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

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.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

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.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

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.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

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.

The English summary is being prepared. Showing the original description.

brycewang-stanford/Awesome-Journal-Skills1.3KUpdated Sep 27, 2026

See all skills by brycewang-stanford

Report a problem with this skill