/simulation-study — Monte Carlo Simulation Study

Design and run a Monte Carlo experiment that characterizes an estimator's finite-sample behavior, then review it for the bugs that quietly invalidate simulation evidence.

Input: $ARGUMENTS — a description of the estimator(s) and DGP to study (e.g., "compare 2SLS vs LIML under weak instruments with heteroskedasticity"), or a pointer to an existing script/paper whose simulation you want to reproduce or extend.

Constraints

• Follow .claude/rules/simulation-conventions.md — the simulation contract (DGP, truth, estimand, MCSE, assumption regime) is non-negotiable.
• Declare the assumption regime in the script header and respect the firewall — an out-of-assumption run never supports a within-assumption claim (simulation-conventions.md §2).
• Follow .claude/rules/r-code-conventions.md for general R standards (header, library() at top, relative paths, numerical discipline).
• Save the script to scripts/R/ with a numbered, descriptive name (e.g., scripts/R/sim_2sls_vs_liml.R).
• Save outputs (per-rep raw tibble, summary table, figures) to scripts/R/_outputs/.
• saveRDS() the per-replication raw results, not just the summary — re-aggregation and the review pass need them.
• Run the sim-reviewer agent on the generated script before presenting results, then address Critical/High findings.