jbes-data-analysis
Use when building the Monte Carlo evidence and the substantive empirical application for a Journal of Business & Economic Statistics (JBES) methods paper. Designs and audits the simulation study and the real-data analysis; it does not derive the asymptotic theory (see jbes-identification-strategy).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jbes-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.
# Monte Carlo & Empirical Application (jbes-data-analysis) ## When to trigger - The asymptotic theory exists but the simulation study is thin or one-sided - The empirical application is a toy illustration rather than a substantive use - Reviewers will ask "does the method actually work in finite samples / on real data?" - You need to choose DGPs, baselines, and an application that show the method's value ## Why this matters at JBES JBES is a methods-with-empirics journal: a contribution is incomplete without **finite-sample evidence** and a **substantive empirical application** in microeconomics, macroeconomics, business, or finance. The simulation study is how you demonstrate the asymptotics bite at realistic sample sizes; the application is how you demonstrate **clear empirical relevance**. Both are evaluated by method experts who will reproduce or interrogate them. ## Monte Carlo design - **DGPs that span the conditions**: include cases that satisfy your assumptions and cases that stress or violate them (dependence, heavy tails, weak identification, high dimension) so the breakdown frontier is visible. - **Sample-size grid**: show size/power/coverage/bias/RMSE converging as n gr
- When to trigger
- Why this matters at JBES
- Monte Carlo design
- The empirical application
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Referee-pushback patterns on the evidence (venue-specific fixes)
- Worked vignette: validating a new long-horizon forecast test
- Evidence pass for Journal of Business & Economic Statistics
- Output format
What does the jbes-data-analysis skill do?
Use when building the Monte Carlo evidence and the substantive empirical application for a Journal of Business & Economic Statistics (JBES) methods paper. Designs and audits the simulation study and the real-data analysis; it does not derive the asymptotic theory (see jbes-identification-strategy).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jbes-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.