From econometrica-skills
Designs and audits Monte Carlo simulation evidence for Econometrica manuscripts, covering finite-sample performance, regularity-condition stress tests, and degenerate cases.
How this skill is triggered — by the user, by Claude, or both
Slash command
/econometrica-skills:ecta-robustnessThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- The paper reports asymptotic theory but contains **no finite-sample (Monte Carlo) check**
For methods papers, asymptotics without finite-sample evidence is a standard rejection reason. The Monte Carlo is not decoration — it is how the reader learns whether the asymptotic approximation is usable at realistic sample sizes.
Econometrica-specific: simulation results fall inside the Econometric Society Data and
Code Availability Policy (which covers "empirical, experimental, and/or simulation
results"). The ES Data Editor will run a pre-acceptance reproducibility check on your
Monte Carlo, so every table must regenerate bit-for-bit from seeded code (see
ecta-replication-package). This is a sharper bar than at applied siblings where simulation
appendices are rarely re-run. A pure-theory paper with no simulations is exempt from that
policy, but numerical illustration is still expected where it sharpens a result.
ecta-replication-package).| Quantity | Why |
|---|---|
| Bias and RMSE / MSE | Point-estimation quality vs. competitors |
| Empirical size at nominal 5% / 10% | Whether the test controls size in finite samples |
| Size-adjusted power / power curves | Whether the test detects departures, fairly compared |
| Coverage and average length of CIs | Whether intervals are valid and informative |
| Sensitivity to tuning (bandwidth, # of moments, penalty) | Whether results hinge on a knob |
| Behavior under weak / near-boundary identification | Whether pointwise asymptotics mislead |
A theory paper still benefits from numerical illustration: plot the equilibrium / value function / comparative-static across the parameter range, show the representation on a worked example, or compute the solution where closed forms are unavailable. Make clear this is illustration, not evidence of generality (the proof carries generality).
【Designs】favorable: ...; boundary/adverse: ...
【Sample sizes】[...] 【Replications】... 【MC error reported】yes/no
【Competitors】[...]
【Metrics】bias/RMSE, size, power, coverage, length — [which reported]
【Tuning sensitivity】...
【Weak/boundary regime】examined / n.a.
【Gaps】[...]
【Next step】ecta-tables-figures
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin econometrica-skillsDesigns Monte Carlo simulations and numerical illustrations for Econometric Theory papers, showing finite-sample behavior tracks asymptotics.
Guides design and audit of Monte Carlo simulations, empirical applications, and estimator comparisons for The Econometrics Journal, focusing on reproducibility and theoretical alignment.
Guides Monte Carlo simulation design and empirical illustration for Journal of Econometrics submissions, covering size/power, DGP stress tests, and computational hygiene.