qe-data-analysis
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill qe-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.
# 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
- When to trigger
- QE expects analysis that is both credible and reproducible
- Analysis discipline by paper type
- Structural / computational
- Empirical (applied micro / finance)
- Experimental / simulation
- Reproducibility scaffolding (build as you go)
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- What QE referees probe in the quantitative core
- Worked vignette: an SMM estimate (illustrative)
- Referee pushback and the analysis fix
- Output format
What does the qe-data-analysis skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill qe-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.