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Fast high-dimensional fixed effects: OLS, Poisson, IV with multi-way FE; DiD (TWFE, did2s, Sun-Abraham); clustered SEs; etable/coefplot/iplot.
Passed 22 of 22 test cases with the skill, and 13 of 22 without it.
and 7 more in the full report
9 fixed13 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to run fixed effects regressions on three different outcome variables (`wage`, `hours`, `benefits`) using the same set of predictors (`experience`, `education`) and state fixed effects. How can I fit all three regressions simultaneously in one pyfixest call?
| Checked | Without skill | With skill |
|---|---|---|
| The formula specifies multiple outcome variables separated by plus signs on the left side of the tilde. | Missed | Passed |
The instructions your model reads when the skill loads.
pyfixest: fast high-dimensional fixed effects estimation for Python. Covers OLS, Poisson, and IV regression with multi-way fixed effects; difference-in-differences estimators (TWFE, did2s, lpdid, Sun-Abraham); clustered standard errors; wild bootstrap; and publication output (etable regression tables, coefplot, iplot event study plots). Use when running fixed effects regressions, difference-in-differences designs, Poisson count models with FE, or producing publication-ready regression tables. For panel random/between effects, use linearmodels; for GLM/time series without FE, use statsmodels.
Comprehensive skill for fixed effects regression, instrumental variables, and difference-in-differences estimation with pyfixest. Use decision trees below to find the right guidance, then load detailed references.
pyfixest is a Python implementation of the R fixest package (Berge, Butts, & McDermott, 2026):
|, IV after second |, multiple estimation via sw()/csw()etable() for regression tables, coefplot() and iplot() for coefficient visualizationThis skill targets pyfixest 0.40.0, the major release aligning with R fixest 0.13. Breaking changes from earlier versions:
"iid" — old code silently produces different SEsssc() arguments renamed: adj → k_adj, fixef_k → k_fixef, cluster_adj → G_adj, cluster_df → G_dffixef_rm default changed from "none" to "singleton" — singletons now dropped by defaultEach topic in ./references/ contains focused documentation:
| File | Purpose | When to Read |
|---|---|---|
quickstart.md | Installation, first regression, formula syntax | Starting with pyfixest |
fixed-effects.md | Multi-way FE, SE types, clustering, wild bootstrap | FE models and inference |
instrumental-variables.md | IV syntax, first stage, weak instruments | IV/2SLS estimation |
difference-in-differences.md | TWFE, did2s, lpdid, Sun-Abraham, event studies | DiD designs |
tables-and-plots.md | etable, coefplot, iplot, dtable | Reporting results |
advanced-inference.md | Wild bootstrap, randomization inference, MHT corrections, Gelbach | Advanced statistical inference |
integration.md | Multiple estimation, Poisson, GLM, marginaleffects, online learning | Advanced features |
gotchas.md | Common errors, v0.40 breaking changes, fixest vs pyfixest | Debugging issues |
test cases passed, vs 13 / 22 without
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