# Full Empirical Analysis Skill Stata

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem, ...

- Repository: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills (4869 stars)
- Category: Testing
- License: see repository
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-Stata -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-Stata -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-Stata -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill-Stata -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills`

## Description

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 

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

1. **Stata idioms, not Python-translated.** `reghdfe`, not "statsmodels analogue of reghdfe". `esttab`, not "Stata's stargazer".
2. **Reproducible .do files.** Every code block below is runnable after `use data.dta, clear`. No Jupyter, no notebooks, just do-files and log files.
3. **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.
4. **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.
5. **Progressive disclosure.** `SKILL.md` gives the canonical command at each step; [`references/`](references/) holds variant-specific depth (dozens of tests, estimator-specific diagnostics, graph recipes).

## SkillOpt-style execution gate

The rest: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/plugins/empirical-analysis-stata/skills/pipeline/SKILL.md