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rfs-empirical-design

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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.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Review-of-Financial-Studies-Skills/skills/rfs-empirical-design/SKILL.md

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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 typeDefault estimator
Treatment effect, panelModern DID estimator + two-way FE as a benchmark
Cross-sectional return premiumFama–MacBeth (with Shanken / GMM correction)
Predictive regressionPanel/pooled with overlap-robust SEs; OOS tests
Risk exposure / factor modelTime-series spanning regressions, GRS test
Structural parameter / counterfactualSMM / 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: feols with 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_hochberg and report the alpha that survives.
  • Fama–MacBeth + Shanken EIV are Stata-canonical — run via mcp__stata-mcp__stata_do with the vendored resources/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