jape-topic-selection

GitHub

辅助评估实证或方法应用论文是否适合发表在《应用计量经济学杂志》(JAE)。通过检查应用性、可复现性及文章类型,判断选题范围契合度。

Journal-of-Applied-Econometrics-Skills/skills/jape-topic-selection/SKILL.md brycewang-stanford/Awesome-Journal-Skills

触发场景

决定将实证论文投稿至 JAE 不确定选题是否过于理论化 在标准研究文章与复现文章之间做选择

安装

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jape-topic-selection -g -y
更多选项

非标准路径

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Econometrics-Skills/skills/jape-topic-selection -g -y

不安装直接使用

npx skills use brycewang-stanford/Awesome-Journal-Skills@jape-topic-selection

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jape-topic-selection -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": "jape-topic-selection",
    "description": "Use when deciding whether a project fits the Journal of Applied Econometrics (JAE) — an applied (not pure-theory) journal publishing empirical, replicable work that applies or develops econometric techniques on real data. Tests scope fit, the replicability requirement, and the Research vs. Replication Article tracks before you commit."
}

Topic Selection for JAE (jape-topic-selection)

When to trigger

  • Choosing where to send an empirical or method-application paper and weighing JAE
  • Unsure whether your idea is "applied enough" or "too theoretical" for JAE
  • Deciding between a standard Research Article and JAE's Replication Article track

JAE's scope test

JAE publishes applied econometrics: papers that apply and develop econometric techniques on real data, with the focus on the application rather than pure econometric theory. A purely theoretical contribution (a new estimator with asymptotics but no real-data application) is off-fit — route it to a methods/theory outlet. A good JAE topic does one of:

  • Applies an established or newly adapted method to a substantive economic question on real data, with credible inference;
  • Develops a technique but anchors it in a real-data application that demonstrates and tests it; or
  • Replicates previously published empirical results (the dedicated Replication Article category), reporting successes and failures.

The non-negotiable filter: replicability

Because accepted papers must deposit data and code in the JAE Data Archive (since 1994, now at ZBW, unless confidential), ask before you start: can the data behind every result be deposited as plain ASCII/CSV with a readme, or at minimum documented enough that others can apply for access? If the data can be neither shared nor described for access, the project is a poor JAE fit.

Fit matrix

Score the candidate on five gates before drafting:

  • Economic question: the paper answers a substantive question, not only demonstrates an estimator.
  • Econometric lesson: readers learn when or why a method changes an applied conclusion.
  • Real-data anchor: the central evidence uses actual data, not only simulation or asymptotics.
  • Replication path: every result can be regenerated from depositable or clearly documentable inputs.
  • Article track: the paper is either a standard applied contribution or a Replication Article; do not mix the two without saying which promise is primary.

If any gate fails, redirect early. JAE fit is strongest when the data, method, and applied question are mutually necessary; it is weakest when one of the three can be removed without changing the paper.

Scope verdict table

Pattern-match the project against recurring candidate shapes:

Project shape JAE verdict Why
New estimator + asymptotics + small empirical illustration Borderline Fit hinges on whether the application carries the paper; if the illustration is decorative, reroute to a theory/methods outlet
Established method, new real-data finding with credible inference Strong fit The venue's bread and butter — provided the deposit is feasible
Pure Monte Carlo comparison of estimators Off-fit alone Anchor the simulations to a real empirical problem or send to a methods journal
Re-examination of a prominent published result using its archived data Strong fit (Replication Article) The dedicated track exists exactly for this
Policy evaluation on administrative data that can never be shared or described Poor fit The mandatory archive deposit cannot be satisfied even via the confidential-data readme route

Choosing a Replication Article target

The track rewards replications of prominent papers — results people teach, cite, or build policy on. Screen a target on four points: (i) the original's data are archived (ideally in the JAE Data Archive) or otherwise reconstructible; (ii) the claim is sharp enough that confirm/qualify/overturn is decidable; (iii) you can separate data-revision effects from coding differences from genuine fragility; (iv) a negative result would still be informative — JAE's track publishes failures as well as successes, which is rare and worth exploiting. Replicating an obscure paper, or one whose data are gone, fails the screen however careful the execution.

Worked fit assessment: two candidates (illustrative)

Candidate A: a new shrinkage estimator with proofs, demonstrated on one well-worn growth dataset where it barely changes the estimates. The real-data anchor is decorative — redirect to a methods venue, or find an application where shrinkage flips a conclusion. Candidate B: quarterly energy-demand elasticities for 14 countries, where switching from textbook HAC to a few-cluster-appropriate bootstrap moves the headline elasticity from "significant at 5%" to marginal — an applied question, an econometric lesson, public data exportable to CSV. B is the JAE paper despite A being technically deeper.

Output format

【Scope】applied on real data? [Y/N] | pure-theory risk? [Y/N]
【Replicable】data depositable or documentable? [Y/N]
【Lesson】method choice changes an applied conclusion? [Y/N]
【Track】Research Article / Replication Article
【Verdict】JAE fit / redirect → where

Supplementary resources

版本历史

  • 1839142 当前 2026-07-05 13:26

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元信息

文件数
0
版本
5717eab
Hash
c809b31f
收录时间
2026-07-05 13:26

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