Jfqa data analysis

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)

Setting	Inference JFQA referees expect	Also show
Firm panel, persistent outcome	two-way cluster (firm and year), or firm cluster with year FE	robustness to the other clustering choice
Fama-MacBeth on monthly returns	Newey-West with the lag count stated and justified	plain FMB SEs for comparison
Staggered policy adoption	cluster at the level of treatment assignment (e.g., state)	event-study leads/lags
Few clusters (roughly < 50)	wild cluster bootstrap p-values	the cluster count itself
Overlapping long-horizon returns	Newey-West/Hodrick lags matched to the horizon	non-overlapping subsample check
Generated regressors (betas, fitted values)	bootstrap or an errors-in-variables correction	the 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