Full-empirical-analysis-skill-Stata

Full-empirical-analysis-skill-Stata is a Statistical modelling skill for AI agents, published by brycewang-stanford in the Auto-Empirical-Research-Skills repository on GitHub. Skillspace rates Full-empirical-analysis-skill-Stata D (56/100) and it passed the safety scan; the rating is based on static analysis only.

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests…

Grade
D
Quality
56 / 100
Safety
Passed
GitHub stars
3,783

Unproven: rated by static analysis only, not yet compared head-to-head.

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View source on GitHubbrycewang-stanford/Auto-Empirical-Research-Skills

Our review

Architecture
66 / 100
Maturity
Structured: ships references or scripts
Security scan
No issues found
License
Not declared. Check the repository before reusing it.

Not checked yet

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