Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› jar-topic-selection
jar-topic-selection
GitHub用于在数据工作前筛选和打磨JAR论文选题，评估其会计属性、识别策略及经济意义。锁定研究问题，不涉理论构建或方法设计，确保符合JAR实证归档传统及数据代码公开要求。
Trigger Scenarios
拥有数据集但缺乏明确的会计研究问题
不确定选题是否适合JAR期刊
问题仅为相关性分析且缺乏会计实质
决定投稿Ray Ball会议或注册报告
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jar-topic-selection -g -y
SKILL.md
Frontmatter
{
"name": "jar-topic-selection",
"description": "Use when choosing or sharpening a research question for a Journal of Accounting Research (JAR) manuscript — testing whether it is a real accounting question with a credible setting, JAR fit, and identification potential before any data work begins. Locks the question; it does not build the mechanism (jar-theory-development) or design the test (jar-methods)."
}
Topic Selection & JAR Fit (jar-topic-selection)
When to trigger
- You have a dataset or a setting but not yet a question that matters for accounting
- You are unsure whether the idea fits JAR versus TAR, JAE, RAST, or CAR
- The question is "is X correlated with Y?" with no clear stakes for accounting
- You are deciding whether to target the Ray Ball JAR Annual Conference or the Registered Reports track
What JAR is looking for
JAR publishes original research in all areas of accounting and accounting-related topics, drawing on finance, economics, statistics, psychology, and sociology. The journal's gravitational center is empirical-archival capital-markets research in the Ball-Brown tradition: how accounting information is produced, disclosed, audited, and used by investors, analysts, creditors, regulators, and managers. A JAR-fit question almost always (a) concerns an accounting/disclosure/auditing/standard-setting phenomenon, (b) lives in a setting with a credible source of identifying variation (a standard change, a regulation, a discontinuity, a shock), and (c) has clear economic stakes — it changes how we think information flows into prices, contracts, or decisions.
The fit test (run before collecting data)
- Accounting question? Is the dependent or independent variable genuinely about accounting information (earnings, disclosure, audit quality, comparability, recognition vs. disclosure, enforcement)? A pure finance or pure economics question belongs elsewhere.
- Setting with leverage? Is there a shock, rule change, threshold, or staggered adoption you can exploit? "We run a panel regression of Y on X" with no identifying variation is a weak JAR start.
- Economic magnitude? Would a referee see the estimate as economically (not just statistically) meaningful?
- Marginal contribution? Does it move beyond a known result, an out-of-sample replication, or a mechanical mediator?
- Data/code feasibility? Because JAR requires posted data and code, confirm you can build a reproducible package from licensed sources (you usually cannot redistribute raw WRDS data).
Sourcing strong JAR questions
- Regulatory and standard-setting shocks (FASB/IASB adoptions, SEC rules, PCAOB inspections) as natural experiments.
- New or under-used disclosure/text data (EDGAR filings, comment letters, conference calls).
- Tensions in theory (e.g., recognition vs. disclosure, transparency vs. proprietary cost) that a clean setting can adjudicate.
- For higher-outcome-risk questions needing new data collection (surveys, field experiments), consider Registered Reports — in-principle acceptance protects a well-designed null.
Checklist
- The question is genuinely about accounting information, not just finance/economics
- A credible identifying setting (shock/rule/threshold/staggered adoption) is in sight
- The expected estimate is economically meaningful, not only significant
- The marginal contribution over existing accounting work is articulable in one sentence
- A reproducible data-and-code package is feasible from accessible sources
- Venue fit confirmed: JAR vs. TAR/JAE/RAST/CAR
- If new-data/high-risk: Registered Reports considered; if conference-relevant: Ray Ball eligibility checked
Anti-patterns
- Data-dredging: starting from "what's in WRDS" rather than a question.
- Finance in disguise: a returns paper with no accounting-information content.
- No identification: a cross-sectional correlation framed as an effect.
- Mechanical relations: regressing a variable on its own construction.
- Replication-only: re-confirming a settled result with newer data and no new economics.
Output format
【Question】one sentence (about accounting information)
【Setting & identifying variation】shock / rule / threshold / staggered adoption / none-yet
【Economic stakes】why the magnitude matters
【JAR fit vs. TAR/JAE/RAST/CAR】fit / redirect because ...
【Track】Regular Manuscript / Registered Report / Ray Ball Conference candidate
【Data-and-code feasibility】reproducible package possible? sources ...
【Next step】jar-theory-development
Version History
- 1839142 Current 2026-07-05 13:25
