ectheory-data-analysis
Use for the Monte Carlo and numerical-illustration component of an Econometric Theory (ET) paper — designing simulations that show finite-sample behavior tracks the asymptotics, plus any illustrative empirical example. Lighter than empirical journals; the spine stays the theory.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ectheory-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.
# Numerical Illustration & Monte Carlo (ectheory-data-analysis) ## When to trigger - Your theorem is proved and you need simulations showing it bites in finite samples - Reviewers will ask whether the asymptotic approximation is accurate at realistic n - You include an illustrative empirical application and want it to serve the theory, not the reverse - The simulation design feels arbitrary and you need principled choices ## Role of "data analysis" at a theory journal ET is theorem-proof first; numerical work is **evidence that the asymptotics are useful**, not the contribution itself. Two distinct, optional components: 1. **Monte Carlo** — the standard companion to a limit result. Its job is to show that finite-sample size/power/bias/coverage track the theory, and to map where the approximation breaks down. 2. **Empirical illustration** — an optional applied example showing the method on real data. It illustrates; it does not carry the paper. Keep it proportionate. ## Designing a credible Monte Carlo for ET - **DGP coverage.** Span the assumptions: include cases near the boundary (weak identification, near-unit-root, growing dimension, heavy tails, dependence) where the theory is
- When to trigger
- Role of "data analysis" at a theory journal
- Designing a credible Monte Carlo for ET
- Reproducible computation
- Checklist
- Anti-patterns
- What an ET referee checks in the Monte Carlo first
- Worked vignette and the simulation fixes
- Output format
What does the ectheory-data-analysis skill do?
Use for the Monte Carlo and numerical-illustration component of an Econometric Theory (ET) paper — designing simulations that show finite-sample behavior tracks the asymptotics, plus any illustrative empirical example. Lighter than empirical journals; the spine stays the theory.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ectheory-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.