Individual skill
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.
Quantitative-Economics-Skills/skills/qe-data-analysis/SKILL.md
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# 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