Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› ectheory-data-analysis
ectheory-data-analysis
GitHub用于计量理论论文的蒙特卡洛模拟与数值说明，验证有限样本行为是否贴合渐近理论。涵盖模拟设计、指标报告、基准对比及复现规范，确保数值工作服务于理论证明而非喧宾夺主。
Trigger Scenarios
需要模拟展示定理在有限样本下的有效性
审稿人质疑渐近近似在现实样本量下的准确性
包含旨在说明理论的实证应用案例
模拟设计缺乏原则性依据
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ectheory-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "ectheory-data-analysis",
"description": "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."
}
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:
- 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.
- 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 most stretched.
- What you report. For an estimator: bias, RMSE, and the gap between empirical and nominal coverage. For a test: empirical size under the null and power under local/fixed alternatives.
- Comparisons. Benchmark against the natural existing method, so the simulation shows what your theory buys.
- Sample sizes. A grid of n that reveals the convergence rate visually, not a single n.
- Honesty. Show where the asymptotic approximation is poor; a candid breakdown region strengthens credibility more than uniformly green tables.
Reproducible computation
- Fix and report random seeds; report the number of Monte Carlo replications and all n.
- Specify the DGP precisely enough to regenerate every table/figure.
- Keep simulation code clean and runnable; long simulation evidence can go to the online Supplementary Material (already-reviewed, separate labeled file, not copyedited).
Checklist
- Monte Carlo DGPs span the assumptions, including the boundary cases
- Reported metrics match the claim (coverage/size/power/bias/RMSE as appropriate)
- A grid of n reveals the rate; convergence visible
- Benchmarked against the natural existing method
- Breakdown region of the approximation shown honestly
- Seeds, replication count, and DGP fully specified
- Empirical illustration (if any) kept proportionate to its illustrative role
Anti-patterns
- A single favorable n and DGP chosen to flatter the method
- Simulations that never probe the boundary where the theory is delicate
- An empirical "application" that overshadows the theorem
- Unreported seeds / replication counts (non-reproducible)
- Reporting size but not power for a test (or vice versa)
What an ET referee checks in the Monte Carlo first
At a theorem-proof venue the referee treats simulations as a stress test of whether the limit approximation is useful, not as the result. The first checks:
| Referee check | Passes for ET | Triggers a revision |
|---|---|---|
| DGP vs assumptions | Spans the boundary (near-unit-root, weak ID, growing dim) | One interior DGP that flatters |
| Metric vs claim | Size and power for a test; coverage, bias, RMSE for an estimator | Size only, or RMSE without coverage |
| Sample sizes | A grid of n that makes the rate visible | A single n hiding slow convergence |
| Honesty | Breakdown region reported | Uniformly green tables, no failure regime |
A Monte Carlo that never visits the regime where the proof's delicate step lives is desk-reject-adjacent.
Worked vignette and the simulation fixes
For a refinement that reduces the error in rejection probability of a t-test from order n^(-1/2) to n^(-1) under local-to-unity asymptotics, report the design:
# Monte Carlo skeleton for the refinement illustration
seed = 20260610 # fixed and reported
reps = 50000 # per cell
n_grid = [50,100,200,400,800]
c_grid = [0,-5,-10,-20] # local-to-unity drift c, root rho = 1 + c/n
# size under H0 (first-order vs refined); power under local alt theta0 + h/sqrt(n)
# show ERP=|size-0.05| decays faster for the refined test; flag breakdown at large |c|
The fixes: "rate without distribution theory" → upstream (route ectheory-identification-strategy), since a
Monte Carlo cannot supply a missing limiting law; "no finite-sample evidence" → add the boundary-spanning
design; "simulations avoid the hard regime" → extend the c-grid into the regime where the proof's delicate
step operates. The ET structure is theorem → proof → simulation; confirm Supplement conventions against the
author guidelines.
Output format
【Components】Monte Carlo / empirical illustration / both
【DGP coverage】boundary cases included? [Y/N]
【Metrics】size / power / coverage / bias / RMSE
【n grid】reveals rate? [Y/N]
【Benchmark】existing method compared? [Y/N]
【Reproducibility】seeds + reps + DGP specified? [Y/N]
【Next step】ectheory-tables-figures
Version History
- 1839142 Current 2026-07-05 12:51
