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rfs-empirical-design
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.
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.
