Full-empirical-analysis-skill-Stata
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 + coeffic
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 00.2-Full-empirical-analysis-skill_Stata --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Guides an agent to perform an eight-step Stata pipeline for empirical analysis, covering data import/cleaning, variable construction, descriptive statistics (Table 1), diagnostics, baseline modeling with multiple estimators, robustness checks, further subgroup analyses, and publication-ready outputs (tables and figures).
How it works
- Data import and cleaning: use/import, destring, misstable, duplicates, merge assert.
- Variable construction: gen/egen/winsor2/xtile/xtset with L./F./D..
- Descriptive statistics & Table 1: tabstat/balancetable/asdoc producing a balance-focused table.
- Diagnostics: perform sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid checks.
- Baseline modeling: run reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe.
- Robustness: conduct bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster analyses.
- Further analysis: subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group.
- Publication-ready outputs: esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined; tables T1–T5 and figures F1–F4.
When to use it
Use when you want a complete Stata empirical analysis pipeline that outputs a full set of publication-ready tables and figures, starting from raw data and ending with a multi-column regression table (M1→M6) and associated diagnostics and visuals.
What it can touch
- Tools referenced:
reghdfe,ivreg2,csdid,did_imputation,eventstudyinteract,sdid,rdrobust,rddensity,synth,synth_runner,psmatch2,teffects,ebalance,coefplot,esttab,outreg2,boottest,ritest,rwolf,bacondecomp,honestdid,binscatter. - Outputs:
.dofiles and publication-ready tables/figures (tex/rtf/xlsx/docx; figures as pdf/png).
Caveats
- Mode B ML causal-inference paths may require Python calls for certain tools; explicit notes cover calling Python via Stata 18 blocks when native Stata commands are missing.
- The workflow assumes Stata-native commands and community packages listed; external dependencies or mode switches are described in the SKILL.
# 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_ru
What does the Full-empirical-analysis-skill-Stata skill do?
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 + coeffic
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 00.2-Full-empirical-analysis-skill_Stata --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.