Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
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rfs-empirical-design
GitHub针对RFS论文实证设计瓶颈，提供样本构建、变量度量、估计量选择及因子设计的决策支持。确保识别可信、代码可复现，并符合RFS对透明度与预注册的高标准要求，解决审稿人关于样本窗口和代理变量的质疑。
Trigger Scenarios
需要决定面板固定效应、Fama-MacBeth或GMM等估计量
组合排序或因子构建方法缺乏依据
关键变量度量存在争议或需辩护代理有效性
审稿人质疑样本选择、时间窗口或数据筛选逻辑
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rfs-empirical-design -g -y
SKILL.md
Frontmatter
{
"name": "rfs-empirical-design",
"description": "Use when sample construction, estimator choice, factor\/portfolio design, or measurement is the bottleneck for a The Review of Financial Studies (RFS) manuscript. Settles design choices that make the identification credible; does NOT pick the identification strategy or run robustness."
}
Empirical & Structural Design (rfs-empirical-design)
When to trigger
- The identification strategy is chosen but sample, variables, and estimator are unsettled
- You must decide between panel FE, Fama–MacBeth, GMM, or a structural estimator
- Portfolio sorts / factor construction choices feel arbitrary
- Measurement of the key variable is contestable (proxy validity)
- A referee will ask "why this sample / this window / this proxy?"
Design decisions that make or break an RFS empirical paper
RFS publishes design-defining empirical templates referees will hold you to — e.g., the q-factor construction in Hou, Xue, and Zhang (2015) "Digesting Anomalies" (RFS 28(3)) and the variance-risk-premium measure in Bollerslev, Tauchen, and Zhou (2009) (RFS 22(11)). Two RFS-specific pressures sharpen every choice below: (1) the public code-release condition means every filter and construction step must be reproducible by a stranger, not just described; (2) the Registered Reports option means a design can be locked at Stage 1, so pre-specify wherever you can.
1. Sample construction
- State the universe, the time span, and every filter, with the resulting N at each step (a sample-attrition table).
- Justify the start/end dates by data availability or regime, not convenience.
- Handle survivorship, look-ahead, and backfill bias explicitly (CRSP/Compustat merge timing, delisting returns, point-in-time fundamentals).
- Winsorize vs. trim: state the rule (e.g., 1%/99%) and apply it consistently.
2. Variable measurement
- For each key variable, give: definition, data source, construction formula, and unit.
- Defend proxy validity — a proxy needs a first-principles or validation argument, not just precedent.
- Avoid mechanical correlation between LHS and RHS (e.g., overlapping accounting items).
3. Estimator choice
| Question type | Default estimator |
|---|---|
| Treatment effect, panel | Modern DID estimator + two-way FE as a benchmark |
| Cross-sectional return premium | Fama–MacBeth (with Shanken / GMM correction) |
| Predictive regression | Panel/pooled with overlap-robust SEs; OOS tests |
| Risk exposure / factor model | Time-series spanning regressions, GRS test |
| Structural parameter / counterfactual | SMM / GMM / MLE with identification argument |
4. Fixed effects and controls
- Saturate fixed effects to absorb the right confounders (firm, industry×year, etc.) — but show the result is not mechanical to the FE choice.
- Distinguish controls that are "bad controls" (post-treatment / outcomes) from legitimate covariates.
5. Standard errors
- Cluster at the level of treatment assignment or the unit of correlation.
- For asset pricing, match SE to the return structure (Newey–West for autocorrelation, Driscoll–Kraay for cross-sectional + serial dependence).
Execution bridge (StatsPAI / Stata MCP)
Run the asset-pricing battery, don't just specify it. Full map:
execution-with-mcp. RFS is finance top-3 (with JF, JFE) — corporate-causal chain for corporate papers, factor-zoo haircut for asset pricing.
- Factor regressions / time-series alphas:
feolswith the right SEs (Newey–West / clustered) — read the alpha and t off the return. - Factor-zoo haircut: after disclosing how many signals were screened, apply
romano_wolf/benjamini_hochbergand report the alpha that survives. - Fama–MacBeth + Shanken EIV are Stata-canonical — run via
mcp__stata-mcp__stata_dowith the vendoredresources/code/(asreg/xtfmb). - Exhibits:
etable; hand formatting to the tables/figures skill.
Report the economic magnitude (bps/month alpha, Sharpe gain); full factor grid → appendix. JF execution walkthrough.
Checklist
- Sample-attrition table present; every filter justified
- Survivorship / look-ahead / backfill addressed (point-in-time data)
- Each key variable has definition + source + formula; proxy validity argued
- Estimator matches the question; benchmark estimator also reported
- FE structure justified; no bad controls
- SE clustering / adjustment matches the data-generating structure
- Every filter/construction step is reproducible from the to-be-released code (RFS condition)
- Design choices are pre-committed where possible (Stage-1-ready), not chosen ex post
Anti-patterns
- An unexplained sample period or unexplained filters that conveniently strengthen results.
- A proxy defended only by "following prior literature" when its validity is in doubt.
- TWFE reported as if it were a modern staggered-DID estimator.
- Controlling for post-treatment outcomes ("bad controls").
- Standard errors that ignore overlapping returns or treatment-level clustering.
Output format
【Sample】universe / span / filters / final N
【Key measures】variable → source → formula → validity note
【Estimator】... (+ benchmark)
【FE & controls】...
【SE structure】...
【Next step】rfs-robustness
Version History
- 1839142 Current 2026-07-05 14:23
