qe-data-analysis
GitHub用于执行或审计量化经济学手稿的核心定量分析，包括估计、数据清洗与推断。确保结果可信且可复现，满足ES期刊编辑的严格审查标准。涵盖结构模型、实证及实验分析，强调代码规范、版本锁定与主脚本生成。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill qe-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "qe-data-analysis",
"description": "Use when executing or auditing the quantitative core of a Quantitative Economics (QE) manuscript — estimation (structural\/GMM\/MSM or causal), moment construction, data cleaning, computation, and inference — so results are credible and reproducible for the ES Data Editor. Runs and checks the analysis; for the identification argument route to qe-identification-strategy."
}
Data Analysis (qe-data-analysis)
When to trigger
- Estimation is running but you need a disciplined plan for moments, solvers, and inference
- Data cleaning / sample construction choices are undocumented or ad hoc
- A structural model's computation (value-function iteration, simulation, optimization) needs validation
- You want the analysis built so it passes the pre-acceptance ES Data Editor reproducibility check on the first try
QE expects analysis that is both credible and reproducible
QE is the Econometric Society's empirically/computationally oriented journal, so the analysis is judged on quantitative credibility and reproducibility together. The ES Data and Code Availability Policy (DCAS-compatible) means the ES Data Editor runs reproducibility checks before final acceptance: raw data, code, and documentation must regenerate every result in the paper and approved appendices. Build the analysis so this is true from the start, not retrofitted. House norms: report standard errors and confidence/coverage sets (no significance asterisks), and for long-running or hard-to-access computations ship simplified/manageable versions and summary output files (QE explicitly encourages this).
Analysis discipline by paper type
Structural / computational
- Solve cleanly: document the algorithm (VFI, policy iteration, projection), grids, tolerances, and convergence criteria.
- Estimate transparently: state the objective (MLE / GMM / MSM / indirect inference), the weighting matrix, starting values, and use multi-start to argue a global optimum.
- Validate: Monte Carlo recovery of known parameters; fit to targeted moments; untargeted-moment checks; sensitivity of estimates to moments.
- Counterfactuals: re-solve the model under the policy; report uncertainty around counterfactual quantities.
Empirical (applied micro / finance)
- Sample construction documented (inclusion rules, merges, missing-data handling) so it is reproducible.
- Match the estimator to the design (modern DID, IV, RDD — see
qe-identification-strategy). - Inference: cluster at the assignment level; wild-cluster bootstrap with few clusters; randomization inference where apt.
Experimental / simulation
- Pre-registered analysis followed; deviations reported. Seeds set and reported for any randomness.
- Document the DGP / experimental data pipeline end to end.
Reproducibility scaffolding (build as you go)
- One master script (
run_all) regenerating every table and figure from raw inputs. - Pin versions:
renv.lock,requirements.txt/conda,Project.toml/Manifest.toml, recorded Statassc/netversions. - Deterministic seeds; logged run times; a README noting any partial-check scope for the Data Editor.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. Quantitative Economics spans structural and applied micro; the chain serves its reduced-form lane, structural estimation uses its own toolkit.
- 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.
Checklist
- Sample / moment construction fully documented and scripted
- Structural: solver + tolerances + multi-start + Monte Carlo recovery reported
- Estimator matched to design; inference reported as SEs / coverage sets (no asterisks)
- Seeds set and reported; results bit-reproducible from raw data via one master script
- Heavy/long computations have manageable versions + summary outputs (QE encourages this)
- Environment pinned; README drafted for the ES Data Editor
Anti-patterns
- A single global optimum claimed from one start point with no multi-start check
- Undocumented data cleaning that cannot be reproduced
- Significance asterisks instead of standard errors / coverage sets
- Leaving the replication package to assemble at acceptance (the check is before acceptance)
- Non-deterministic results with no seed control
What QE referees probe in the quantitative core
| Probe | What clears it at QE |
|---|---|
| Optimum global? | multi-start grid + objective across starts |
| Recovers truth? | Monte Carlo with known parameters; bias, coverage |
| Computation accurate? | tolerances, grid-refinement check, residual errors |
| Estimates robust? | re-estimate under alternative moments |
| Data Editor can reproduce? | one run_all, pinned environment, logged seeds |
The defining QE failure mode is numerical accuracy left unvalidated — a counterfactual reported to three digits the grid cannot support. Treat numerical error like sampling error: bound it and report it.
Worked vignette: an SMM estimate (illustrative)
A paper estimates an adjustment-cost parameter by simulated method of moments, targeting investment-rate variance and serial correlation. Suppose the headline counterfactual — removing a subsidy lowers aggregate investment 8% — shifts to 5% when the grid doubles from 100 to 200 capital nodes. That 3-point swing is a numerical artifact a referee will catch. Fix: refine the grid until the counterfactual is stable, show a global minimum across 50 starts, and attach a sensitivity matrix. (Illustrative.)
Referee pushback and the analysis fix
- "The structural estimates are not credibly identified." → Add a sensitivity matrix mapping parameters to moments; show targeted-moment fit and untargeted validation.
- "Numerical accuracy is not validated." → Report tolerances, a grid-refinement check, and stability to the digits shown.
- "Results are not robust to specification." → Re-estimate under alternative moments and sub-samples; tabulate the headline movement.
Output format
【Paper type】structural / empirical / experimental / simulation
【Estimation】objective + solver/tolerances + multi-start? [Y/N]
【Validation】Monte Carlo recovery / moment fit / design diagnostics
【Inference】SEs / coverage sets (no asterisks); clustering if any
【Reproducibility】master script + pinned env + seeds? [Y/N]
【Next step】qe-tables-figures
Version History
- 1839142 Current 2026-07-05 14:17
