Full Empirical Analysis Skill Stata
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem, ...
Install
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-Stata -g -a claude-code -y- Repository
- brycewang-stanford/Auto-Empirical-Research-Skills4.9k
- Category
- Testing
- Updated
- 6d ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
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 (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also
Full Empirical Analysis, Classical Stata Workflow
This skill is the canonical 8-step pipeline an applied economist runs on every empirical paper, written in the traditional Stata ecosystem, native Stata + the 20+ community commands that have become de-facto standards (reghdfe, ivreg2, csdid, did_imputation, eventstudyinteract, sdid, rdrobust, rddensity, synth, synth_runner, psmatch2, teffects, ebalance, coefplot, esttab, outreg2, boottest, ritest, rwolf, bacondecomp, honestdid, binscatter).
Companion skills: if the user wants the same pipeline in Python, route to 00-StatsPAI_skill (agent-native DSL) or 00.1-Full-empirical-analysis-skill (explicit Python stack). This skill is the Stata counterpart, every step produces a .do file you can hand to a journal's replication office or a co-author who refuses to leave Stata.
Philosophy
- Stata idioms, not Python-translated.
reghdfe, not "statsmodels analogue of reghdfe".esttab, not "Stata's stargazer". - Reproducible .do files. Every code block below is runnable after
use data.dta, clear. No Jupyter, no notebooks, just do-files and log files. - Full pipeline, not just regressions. Stata users historically over-invest in Step 5 (modeling) and under-invest in Steps 1, 4 and 6, 8. This skill treats them as first-class.
- Rich outputs. Every step yields at least one table (
.tex/.rtf) or figure (.pdf/.png), never a coefficient printed to the Results window and forgotten. - Progressive disclosure.
SKILL.mdgives the canonical command at each step;references/holds variant-specific depth (dozens of tests, estimator-specific diagnostics, graph recipes).
