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](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

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