jbf-data-analysis

name	jbf-data-analysis
description	Use when building or auditing the empirical data and estimation pipeline for a Journal of Banking & Finance manuscript, including financial datasets, bank panels, winsorization, fixed effects, robustness, and reproducible scripts.

Data Analysis (jbf-data-analysis)

When to trigger

• You are constructing the sample, variables, or estimation pipeline
• Results need robustness, heterogeneity, mechanism, or economic-magnitude checks
• Proprietary finance datasets require a reproducible but non-redistributable workflow

Data construction

1. Document every source: CRSP, Compustat, Call Reports, BankFocus, Dealscan, TRACE, OptionMetrics, FDIC, SEC EDGAR, FRED, or private hand-collected data.
2. Define the unit of observation: bank-quarter, firm-year, loan facility, security-day, country-year, event-firm, etc.
3. Show sample attrition: raw data, filters, merges, missing variables, winsorization, final sample.
4. Name variable construction rules: scaling, deflation, lagging, exchange rates, identifiers, and industry/bank classifications.
5. Separate proprietary raw data from shareable code so the replication package can be legal and useful.

Estimation checklist

• Use fixed effects and clustering that match the design.
• Report economic magnitudes in finance units: basis points, percentage of assets, capital ratio points, loan-spread basis points, abnormal returns, default odds.
• Provide robustness over winsorization, sample windows, variable definitions, and alternative outcome measures.
• For event studies, report CAR/BHAR windows and benchmark choices.
• For bank panels, test sensitivity to crisis periods, large banks, mergers, and regulatory regime changes.

Reproducibility

• Keep a single run_all entry point that regenerates tables and figures.
• Pin software versions and random seeds.
• Store intermediate files only when they materially reduce runtime; document how they are made.
• Prepare a data-access README for licensed sources.

Bank-panel stress checks

For bank/intermediation panels, add targeted checks for:

• crisis-period sensitivity;
• large-bank or systemically important institution influence;
• mergers and identifier breaks;
• regulatory regime changes;
• balance-sheet scaling and winsorization choices.

Report which checks are main-text, appendix, or archive-only.

Dataset-to-question matrix

Source	Unit	JBF expectation	Caveat to pre-empt
US Call Reports / FR Y-9C	bank- or BHC-quarter	merger-adjusted series; top-holder aggregation choice stated	identifier breaks across RSSD changes
Orbis Bank Focus (ex-BankScope)	bank-year, cross-country	consolidation-code filters documented	duplicated statements across consolidation levels
DealScan	loan facility	facility vs package level stated; lead-arranger roles defined	borrower link tables need documented match rates
FDIC SDI / failure data	bank-quarter	survivorship handling for failed and acquired banks	de novo entrants and charter conversions
Cross-country regulation surveys	country-wave	survey-wave timing matched to the outcome window	self-reported regulation measures

Worked sample build (illustrative)

Target: bank-quarter panel for a liquidity-regulation study, 2005Q1–2019Q4.

• Raw Call Reports: 612,000 bank-quarters (illustrative count).
• Drop de novo banks (<5 years), foreign branches, and banks under $100 million in assets: −118,000.
• Merger-adjust around RSSD changes; drop quarters with >50% asset jumps: −24,000.
• Require non-missing CET1, loans, and core deposits; winsorize ratios at 1/99: final ≈465,000.
• Put exactly this attrition table in the appendix — JBF referees read it before the regressions.

Pipeline skeleton

01_pull_callreports.*   # raw downloads, data vintage recorded
02_merger_adjust.*      # RSSD link table + asset-jump audit
03_build_panel.*        # ratios, lags, winsorization flags
04_baseline.*           # FE + clustering per jbf-identification-strategy
05_robustness.*         # crisis splits, large-bank drops, alt definitions
06_export_exhibits.*    # tables/figures numbered as in the manuscript

Economic-magnitude benchmarks for bank panels

• Loan growth: report effects relative to sample-mean quarterly growth, not only the raw coefficient.
• Capital: percentage points of CET1, anchored to the regulatory minimum or buffer.
• Spreads: basis points relative to the mean all-in-drawn spread.
• Risk: change in Z-score or NPL ratio relative to the cross-sectional standard deviation.
• Funding: percentage points of the core-deposit or wholesale-funding share.

Referee data pushbacks

• "Are results a 2008–09 artifact?" → re-estimate excluding 2007Q3–2009Q4 and report both estimates.
• "Bank Focus duplicates inflate your N." → show the consolidation-filter step with before/after counts.
• "DealScan spreads ignore fees." → use the all-in-drawn spread and say so in the variable definitions.
• "Winsorizing at 1/99 hides outliers." → also show 2.5/97.5 and trimmed samples in an appendix column.
• "Results hinge on the largest banks." → report with and without the systemically important institutions.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JBF is empirical banking/finance — corporate/bank causal designs around regulation and shocks.

• Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_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 exact suggest_function for each.
• Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.

Output format

[Sample] unit + period + observations
[Data sources] ...
[Key variables] ...
[Main estimator] ...
[Robustness queue] ...
[Reproducibility gaps] ...
[Next step] jbf-tables-figures