ectj-data-analysis
Use when designing or auditing The Econometrics Journal (EctJ) Monte Carlo simulations, empirical applications, estimator comparisons, robustness checks, computation, seeds, and applied-value evidence.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ectj-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.
# EctJ Data Analysis Use this when the method has to prove both statistical behavior and empirical usefulness. ## Analysis checks - Keep Monte Carlo evidence focused. RES guidance asks that simulation results be summarized compactly in the main text; use the supplement for details. - Include an empirical application that demonstrates applied value, even for theory-heavy work. - Align simulations with the assumptions and failure modes from the theory section. - Compare against credible econometric alternatives, not only simplified baselines. - Report sample sizes, data-generating processes, tuning, seeds, software versions, runtime, and convergence or failure diagnostics. - Show where the new procedure changes an applied conclusion, uncertainty interval, test decision, or policy-relevant estimate. ## Minimum evidence map Before drafting results, create a one-page map with these rows: - **Theory target**: theorem, proposition, approximation, or diagnostic the simulation is meant to stress. - **DGP grid**: the smallest parameter grid that probes the boundary cases, not every imaginable design. - **Competitors**: incumbent estimator/test plus at least one strong practical alternative.
- Analysis checks
- Minimum evidence map
- Reproducibility ledger
- Theory-to-simulation contract
- Anchoring the DGP in the application
- Computation reporting floor
- Execution bridge (StatsPAI / Stata MCP)
- Output format
What does the ectj-data-analysis skill do?
Use when designing or auditing The Econometrics Journal (EctJ) Monte Carlo simulations, empirical applications, estimator comparisons, robustness checks, computation, seeds, and applied-value evidence.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ectj-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.