jfi-data-analysis
Use when planning or stress-testing the analysis behind a Journal of Financial Intermediation (JFI) paper — bank/loan-level panel work, demand-absorbing specifications, and the robustness battery for empirics, or numerical examples and calibrated illustrations for theory. It guides the analysis plan; it does not replace running the code.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfi-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Analysis (jfi-data-analysis) ## When to trigger - Building the empirical analysis on bank/firm/loan data, or its robustness battery - Building a numerical example or calibration that illustrates a model's mechanism ## Empirical track (banking data) - **Sample construction:** document the universe (e.g., Call Reports / FR Y-9C banks, DealScan loans, HMDA mortgages), merge keys, and every filter; intermediation samples are sensitive to mergers, charter changes, and reporting breaks. - **Variables:** define balance-sheet and credit quantities precisely (levels vs. growth, winsorizing, deflation); state timing relative to the shock to avoid mechanical reverse causality. - **Specifications:** high-dimensional fixed effects (reghdfe / fixest); for credit-supply questions, use firm×time effects in matched lender–borrower panels to absorb demand. - **Robustness:** alternative samples and windows, placebo periods, leave-one-out by large institutions, alternative clustering, and a balance/parallel-trends check for DID. The expected battery is substantial but there is no fixed robustness-table count; keep the main text compact and push secondary checks to appendices. ## Theory track (n
- When to trigger
- Empirical track (banking data)
- Theory track (numerical illustration)
- Data sharing (both tracks)
- Dataset-to-mechanism decision table
- Worked robustness pass: a capital-shock battery (illustrative)
- Analysis probes specific to this venue
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
What does the jfi-data-analysis skill do?
Use when planning or stress-testing the analysis behind a Journal of Financial Intermediation (JFI) paper — bank/loan-level panel work, demand-absorbing specifications, and the robustness battery for empirics, or numerical examples and calibrated illustrations for theory. It guides the analysis plan; it does not replace running the code.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfi-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.