jmcb-empirical-design
Use when bank/central-bank data construction, measurement, or sample design is the bottleneck for a Journal of Money, Credit and Banking (JMCB) manuscript. Hardens how the dataset is built and measured so the identification can do its job; it does not re-argue the causal strategy or write prose.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmcb-empirical-design --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.
# Empirical Design (jmcb-empirical-design) ## When to trigger - The dataset is assembled from Call Reports, Y-9C, supervisory, credit-register, or central-bank sources and the construction is under-documented - The key variable (a "monetary shock," a "bank capital ratio," a "credit-supply" measure) is a constructed object whose definition matters for the result - Sample period, window, or frequency choices are not motivated and could be driving the finding - Restricted-access bank/central-bank data are involved and the access path is unstated - A referee questioned whether the measurement, not the mechanism, produces the result ## The JMCB measurement bar JMCB carries a deep replication heritage — the journal's own 1980s–2000s Data Archive episodes (Dewald–Thursby–Anderson; the 2006 "Got Replicability?" audit) made it acutely aware that monetary/banking results often hinge on how series are spliced, deflated, and aligned. So referees scrutinize **construction and timing**: how a series is seasonally adjusted, how regulatory definitions changed mid-sample, how a bank merger reshapes a panel, and whether the announcement window for a monetary surprise is defensible. The standard is t
- When to trigger
- The JMCB measurement bar
- Construction craft by data type
- Bank micro-data (Call Reports / Y-9C / credit registers)
- Monetary / macro series
- Central-bank / supervisory / restricted data
- Sample and specification hygiene
- The construction decisions referees probe most
- From measurement to a credible replication path
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Public-data first, restricted-data when the mechanism demands it
- Worked vignette (illustrative)
What does the jmcb-empirical-design skill do?
Use when bank/central-bank data construction, measurement, or sample design is the bottleneck for a Journal of Money, Credit and Banking (JMCB) manuscript. Hardens how the dataset is built and measured so the identification can do its job; it does not re-argue the causal strategy or write prose.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmcb-empirical-design --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.