Individual skill
jfqa-data-analysis
Use when running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES), winsorizing, fixed effects, clustered and Newey-West standard errors, robustness, and heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code. For theory papers, lighten this and document numerical examples instead.
Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis/SKILL.md
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# JFQA Data Analysis (jfqa-data-analysis)
Use this skill to execute and document the estimation for a **JFQA** empirical finance paper so it is both credible and reproducible from the code you will archive (see jfqa-replication-and-data-policy).
## Data construction (finance-specific)
- Build from standard sources (CRSP, Compustat, CRSP/Compustat Merged, TAQ, IBES, TRACE, OptionMetrics) and document every filter (share codes, exchanges, financials/utilities exclusions, delisting returns).
- **Winsorize or trim** outliers and disclose the cutoffs; finance variables (ratios, returns) have heavy tails.
- Report the sample period, the number of firms and observations, and the unit of analysis.
## Estimation & inference
- Use fixed effects appropriate to the question; justify the **clustering** dimension (firm, time, or two-way) — finance referees will ask.
- For asset-pricing tests, use **Fama-MacBeth** with Newey-West or the appropriate correction; for panels, cluster-robust SEs.