joe-data-analysis
GitHub针对计量经济学方法论文，设计蒙特卡洛模拟与实证说明。涵盖规模/功效检验、DGP压力测试及调参敏感性分析，确保有限样本证据严谨，辅助证明理论在现实数据中的适用性。
触发场景
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joe-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "joe-data-analysis",
"description": "Use when designing the Monte Carlo study and empirical illustration that demonstrate a Journal of Econometrics (JoE) method works in finite samples. Covers size\/power simulation design, DGP stress tests, and the role of the applied illustration relative to the theory."
}
Monte Carlo & Empirical Illustration (joe-data-analysis)
When to trigger
- The theorems are settled but the finite-sample evidence is thin or one-off
- A simulation reports point estimates but no size/power, or never stresses the assumptions
- You are unsure how large or how diverse the Monte Carlo design must be
- You have an empirical illustration but it is doing the wrong job (over- or under-claiming)
What "data analysis" means at a methodology journal
At the Journal of Econometrics the empirical work serves the method, not the other way around. A theorem describes behavior as $n\to\infty$; the Monte Carlo shows the asymptotics bite at realistic sample sizes, and the empirical illustration shows the method is usable and yields a sensible answer on real economic data. The applied illustration is a demonstration, not the paper's primary contribution — purely applied work without a methodological advance is out of scope here. Build both as evidence that the formal claims hold.
Monte Carlo design
Report the right quantities
- Estimators: bias, RMSE, coverage of confidence intervals.
- Tests: empirical size at nominal 5%/10%, then size-adjusted power curves. Over-rejection that vanishes only at huge $n$ is a finding, not a footnote.
- Compare against the nearest existing method on identical DGPs (ties back to
joe-literature-positioning).
Stress the assumptions, do not flatter them
- Vary sample size (including small $n$ where asymptotics may fail).
- Vary the DGP: error distributions (heavy tails, heteroskedasticity), dependence (serial/cluster/spatial), degree of endogeneity or identification strength, dimension.
- Vary tuning parameters (bandwidth, lag length, penalty, number of moments) and show sensitivity.
- Include designs near the boundary of your conditions — that is where referees look.
Computational hygiene
- Fix and report seeds; report replication count and Monte Carlo standard errors so differences are not noise.
- Parallelize heavy designs; record runtime/hardware for the replication archive.
Finite-sample stress grid
Build the Monte Carlo grid around the theorem's weak points, not around flattering defaults:
| Dimension | Minimum stress case |
|---|---|
| Sample size | A small or moderate $n$ where the asymptotic approximation is plausibly strained. |
| Identification strength | Weak instruments, near-collinearity, boundary parameters, local-to-zero effects, or sparse support as relevant. |
| Error process | Heavy tails, heteroskedasticity, serial/cross-sectional dependence, or clustering that matches the target application. |
| Tuning | Bandwidth, penalty, lag, moments, sieve dimension, or bootstrap choice varied enough to show stability. |
| Competitor | The closest existing estimator/test run on exactly the same DGP and reporting scale. |
Pre-register the cells in the simulation plan, then mark any post-hoc additions as diagnostics. JoE referees punish Monte Carlos that prove only that the authors found a friendly DGP.
Empirical illustration
- Pick a dataset where the method's advantage is visible (the problem it solves actually occurs).
- Show the method changes a conclusion or sharpens inference relative to standard practice — that is the payoff.
- Keep claims proportionate: this is an illustration of the tool, not a causal study. Do not oversell the applied finding.
- Cite the data with the Elsevier
[dataset]tag and prepare materials for the archive (seejoe-replication-and-data-policy).
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. Journal of Econometrics is a methods venue — estimator validity + simulation evidence are the contribution; pair estimates with diagnostics and Monte-Carlo where relevant.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Anti-patterns
- A single DGP at one sample size "confirming" the theory
- Reporting raw power without empirical size (size-distorted power is meaningless)
- Hiding tuning-parameter sensitivity or boundary cases
- An empirical section that drifts into an applied paper the method only decorates
- Unreported seeds / replication counts, so results are not reproducible
Output format
【MC estimators】bias / RMSE / coverage reported? [Y/N]
【MC tests】size at 5%/10% + size-adjusted power? [Y/N]
【DGP stress】distributions / dependence / tuning / boundary? [list]
【Benchmark】compared to nearest method on same DGP? [Y/N]
【Reproducibility】seeds + reps + MCSE reported? [Y/N]
【Illustration】method changes/sharpens a real conclusion? [Y/N]
【Next step】joe-tables-figures
版本历史
- 1839142 当前 2026-07-05 13:31
