Jcf data analysis
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Use when building and running the empirical analysis for a Journal of Corporate Finance (JCF) paper — assembling WRDS-era firm panels (Compustat/CRSP/SDC/DealScan), constructing variables, estimating with fixed effects and clustered errors, and layering robustness. It guides the empirical build; pair it with jcf-identification-strategy for the design itself.
SKILL.md
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Data Analysis (jcf-data-analysis)
When to trigger
- Assembling the firm-level panel and constructing corporate-finance variables
- Choosing estimators, fixed effects, and clustering
- Planning the robustness battery a JCF referee will expect
Data build (empirical corporate finance)
- Core sources: Compustat (fundamentals, leverage, payout), CRSP (returns/event windows), CCM (linked), SDC/Refinitiv (M&A, IPO/SEO, repurchases), DealScan (loans/covenants), BoardEx/ISS/Execucomp (governance, pay). Respect WRDS/vendor licenses — never redistribute raw vendor data.
- Document sample construction (filters, financials/utilities exclusions, fiscal-year alignment), winsorizing (e.g., 1/99), and every variable's formula.
Estimation conventions
- High-dimensional fixed effects (
reghdfe/fixest): firm, year, and often industry×year. - Cluster standard errors at the firm level (and/or two-way firm-and-year) consistent with the variation.
- For staggered-adoption DID, use modern estimators (see jcf-identification-strategy), not plain TWFE.
- Report economic magnitudes, not just significance — JCF readers want the size of the effect on a corporate-finance outcome.
Robustness the referee expects
- Alternative variable definitions and subsamples (size, period, industry)
- Alternative fixed-effects / clustering structures
- Endogeneity probes consistent with the design (placebo, falsification, alt. instrument)
- Sensitivity to outliers / winsorizing thresholds
- A short, honest discussion of remaining limitations
Reproducibility (ties to data policy)
- One master script (
run_all) regenerating every table/figure from the build. - Pin software/package versions; set seeds for bootstrap/simulation.
- Prepare the Elsevier Option C data-availability statement and code/variable-construction archive now, not at acceptance (see jcf-replication-and-data-policy).
Firm-panel audit ledger
Before adding robustness, build a ledger:
Claim | Firm sample | Event/date rule | Variable formula | FE/clustering | Output file
Use it to catch the most common JCF weaknesses:
- fiscal-year alignment changes treatment timing;
- financial firms/utilities exclusions alter the estimand;
- winsorization or missing-value rules drive the result;
- two-way clustering is needed but only one dimension is reported;
- an event-window result uses CRSP/Compustat links that are not documented.
Every main result should also have an economic magnitude row. If the table cannot say what a coefficient means for leverage, payout, investment, governance, innovation, or financing constraints, the result is not ready for a corporate-finance audience.
Variable-construction crosswalk (Compustat conventions)
JCF referees read variable definitions before coefficients. Anchor each outcome to a recognized construction and disclose deviations:
Outcome | Conventional construction | Disclosure trap
Book leverage | (dltt + dlc) / at | Mixing book and market denominators across tables
Market leverage | debt / (debt + prcc_f x csho) | Stale prices at fiscal-year ends
Payout | dv and prstkc scaled by assets or earnings | Repurchase proxy ignores option-related buybacks
Investment | capx / lagged at (or PP&E) | Intangibles-heavy firms mismeasured; say so
Cash holdings | che / at (or net assets) | Net-of-cash denominators change percentile meaning
Tobin's q | (at + mkt equity - book equity) / at | A dozen variants exist — name yours and cite it
Board independence | independent directors / board size (BoardEx/ISS) | Vendor classification shifts over sample years
CEO pay | ExecuComp tdc1 | Option-valuation regime breaks across decades
Worked build: a board-reform firm panel
Hypothetical (numbers illustrative): a governance mandate forces some boards to hit an independence threshold. Build trace: Compustat–CRSP universe 1996–2020 → drop financials (SIC 6000–6999) and utilities (4900–4999) → require BoardEx coverage → final panel of 2,400 firms and 21,000 firm-years. Treated = firms below the threshold pre-mandate (38% of the panel). Ledger rows written before estimation: the treatment-date rule (fiscal years ending after the compliance deadline), the independence formula, the FE plan (firm and industry-by-year), clustering (firm). When the headline says investment rises 1.1 percentage points of assets for treated firms, the magnitude row converts it: about 12% of the sample mean — a number a JCF reader can judge.
Estimation pushback JCF referees raise
- "Cluster by firm AND year." → Re-estimate two-way; if inference survives, report it in the main table, not a footnote.
- "Your treated firms chose to be below the threshold." → Add a selection-into-treatment discussion plus a covariate-trend table comparing treated/control before the mandate.
- "Winsorizing drives this." → Re-run at 0.5/99.5, 2/98, and trimmed; one appendix table, three columns.
- "Industry-by-year shocks explain it." → Show the coefficient with and without industry × year FE side by side; if it dies, the paper has a confounding problem, not a formatting one.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JCF is corporate finance — endogeneity of corporate policies is the central threat; foreground IV/DiD identification.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Anti-patterns
- Kitchen-sink controls with no design and no magnitudes.
- Hidden sample-selection steps that change the result.
- Significance-only reporting with no economic interpretation.
Output
【Panel】sources + filters + winsor: documented? [Y/N]
【Spec】FE = <…>; cluster = <…>; estimator = <…>
【Robustness】<list run / planned>