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jfqa-data-analysis

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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.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/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.
  • Report economic magnitudes (e.g., effect of a one-SD change, basis points, alpha per month), not just significance stars.

Robustness & heterogeneity

  • Alternative samples, alternative variable definitions, alternative fixed effects and clustering.
  • Subsample/heterogeneity cuts motivated by the mechanism, not fishing.
  • Placebo or falsification tests where the design allows.

Reproducibility discipline

  • One master script regenerating every table/figure from raw (or pseudo) data.
  • Pin software/package versions; set and report seeds for any bootstrap/simulation.
  • Keep the pipeline archive-ready as you go — JFQA may run random external code verification.

Theory papers

If the paper is theoretical, lighten this skill: replace empirical estimation with reproducible numerical examples / calibrations that illustrate the propositions, and document the computation so a reader can rerun it.

Standard-error decision grid (the first thing a JFQA referee checks)

SettingInference JFQA referees expectAlso show
Firm panel, persistent outcometwo-way cluster (firm and year), or firm cluster with year FErobustness to the other clustering choice
Fama-MacBeth on monthly returnsNewey-West with the lag count stated and justifiedplain FMB SEs for comparison
Staggered policy adoptioncluster at the level of treatment assignment (e.g., state)event-study leads/lags
Few clusters (roughly < 50)wild cluster bootstrap p-valuesthe cluster count itself
Overlapping long-horizon returnsNewey-West/Hodrick lags matched to the horizonnon-overlapping subsample check
Generated regressors (betas, fitted values)bootstrap or an errors-in-variables correctionthe uncorrected SEs flagged as such

An unjustified clustering choice is among the most common JFQA referee complaints; pre-empt it in the table notes, not just the text.

Worked pass: a corporate-finance panel (numbers illustrative)

Hypothetical study of cash holdings and supplier concentration. Sample: Compustat 1990-2023, financials (SIC 6000-6999) and utilities (4900-4999) dropped, ratios winsorized at the 1st/99th percentiles. With firm and year fixed effects and two-way clustering, the standardized coefficient is 0.021 (t = 3.4): a one-SD rise in concentration moves cash/assets by 2.1 pp, about 12% of the 17.5 pp sample mean. The JFQA-grade write-up reports the 12%-of-mean line next to the t-stat, names the clustering in the note, and adds a falsification on firms with nationally diversified suppliers where the mechanism predicts nothing.

Filter log the referee will try to reconstruct

  • CRSP: share codes 10/11; the exchange universe stated; delisting returns merged and the treatment of missing delisting returns disclosed.
  • Compustat: accounting data lagged so it was publicly available at the return date; duplicate gvkey-period rows resolved.
  • Linking: CCM link table with valid link-date ranges — never name matching.
  • Any price or size screens (e.g., penny-stock exclusions) disclosed and shown not to drive the result.
  • Each filter's observation loss tracked so the sample-construction table sums from raw pulls to the final N.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JFQA is empirical finance (asset pricing + corporate) — the DiD / IV / RDD chain for corporate causal claims, the factor-zoo haircut for cross-sectional pricing.

  • Many outcomes / specifications: romano_wolf (step-down FWER, accounts for cross-test correlation) or benjamini_hochberg — report the adjusted threshold.
  • OVB sensitivity: oster_delta / sensemakr — the confounder strength that would overturn the headline.
  • Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley.
  • Re-fit off one handle: audit_result(result_id) lists the missing checks and the exact suggest_function for each — no guessing the battery.
  • Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive (now actually-run) battery in the appendix. See the executed chain in the JF execution walkthrough.

Output format

【Sample】sources, filters, period, N firms/obs
【Estimator】FE / FMB / DID / IV + clustering justified
【Magnitudes】economic effect sizes reported
【Robustness】samples / definitions / placebos
【Next step】jfqa-tables-figures