Individual skill
ecta-robustness
Use when an Econometrica manuscript needs finite-sample evidence and edge-case scrutiny — Monte Carlo design, finite-sample performance, regularity-condition stress tests, and degenerate cases. Designs and audits the simulation evidence; it does not derive the asymptotics (use ecta-identification) or format the resulting tables (use ecta-tables-figures).
Econometrica-Skills/skills/ecta-robustness/SKILL.md
Source commit 932eb23331721b16e967c59f093781be1928b498
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,403
- Repository last updated
- Sep 27, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- academic-writing Repository topic
- agent-skills Repository topic
- journal Repository topic
- academic-research Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- awesome-list Repository topic
- claude Repository topic
- claude-code Repository topic
- economics Repository topic
- design Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 6,743 bytes
Read excerpt
# Monte Carlo and Finite-Sample Evidence (ecta-robustness)
## When to trigger
- The paper reports asymptotic theory but contains **no finite-sample (Monte Carlo) check**
- Coverage / size / power of a proposed test or interval is claimed but never simulated
- You have not probed where the regularity conditions bind or where the method breaks
- A theory result needs numerical illustration of comparative statics or equilibrium behavior
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